Machine Learning Data Scientist – Research Translation & Prototyping

📍 Job Overview

Job Title: Machine Learning Data Scientist – Research Translation & Prototyping

Company: Blueprint Technologies

Location: Redmond, WA

Job Type: Full-time

Category: Data Science / Machine Learning / AI Research Translation

Date Posted: 2026-06-24T21:25:16

Experience Level: 5-10 years

Remote Status: Remote OK (Primary focus on Washington State)

🚀 Role Summary

• This role is pivotal in bridging the gap between advanced AI research and practical, deployable solutions, focusing on the translation and prototyping of cutting-edge machine learning concepts.

• You will be instrumental in evaluating emerging AI technologies, building rapid proof-of-concepts, and developing machine learning models that transform experimental ideas into tangible tools and user experiences.

• Success hinges on a strong blend of technical expertise in machine learning and software engineering, coupled with a proactive approach to experiment design, execution, and validation in dynamic, often ambiguous environments.

• The position requires a hands-on builder who can leverage data, research findings, and novel ideas to drive measurable outcomes and accelerate the adoption of new technologies.

📝 Enhancement Note: The role title "Machine Learning Data Scientist – Research Translation & Prototyping" and the description strongly indicate a focus on applied AI research and development, rather than traditional data science for business analytics. The emphasis on "prototyping," "validating new technologies," and "transforming experimental concepts" points to an innovation-focused position within Blueprint Technologies' product or services division, aiming to capitalize on emerging AI trends.

📈 Primary Responsibilities

• Collaborate closely with research scientists, engineers, designers, and product stakeholders to rigorously evaluate the practical value of emerging AI and machine learning technologies.

• Design, develop, and implement sophisticated machine learning models, AI-powered applications, and experimental systems to test and validate research hypotheses.

• Build rapid prototypes and proof-of-concept (POC) solutions to swiftly validate new technologies, research concepts, and potential product features.

• Fine-tune, benchmark, validate, and continuously improve machine learning models using real-world datasets to ensure optimal performance and effectiveness.

• Develop comprehensive evaluation frameworks, robust benchmarks, and clear success metrics for AI systems, including foundation models, generative AI solutions, multimodal experiences, and agent-based workflows.

• Design and execute quantitative and qualitative experiments to meticulously assess model performance, user engagement, technology adoption rates, and overall solution effectiveness.

• Analyze complex system requirements, meticulously document technical specifications, and develop robust software solutions that are tightly aligned with project objectives and business goals.

• Gather, process, and analyze diverse datasets to extract actionable insights that inform strategic decision-making and guide further development.

• Evaluate, troubleshoot, and optimize machine learning pipelines, AI systems, and software implementations to enhance efficiency, scalability, and reliability.

• Develop, test, and maintain high-quality software applications and the supporting infrastructure necessary for their operation.

• Create and execute comprehensive test plans, perform thorough unit testing, and actively support quality assurance efforts throughout the development lifecycle.

• Support the deployment, validation, and post-implementation monitoring of developed solutions, proactively resolving issues identified during testing and rollout phases.

• Stay abreast of the latest advancements in machine learning, generative AI, multimodal systems, agentic workflows, and related research areas to identify and leverage opportunities for innovation and application.

📝 Enhancement Note: The depth and breadth of responsibilities listed, particularly those involving developing evaluation frameworks, designing experiments, and staying current with advanced AI research areas like foundation models and agentic workflows, indicate a senior-level position requiring significant autonomy and expertise. The focus on translating research into prototypes suggests a strong emphasis on innovation and rapid iteration.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Computer Engineering, Data Science, Mathematics, Statistics, or a closely related technical field.

Experience: 5–7+ years of progressive professional experience in machine learning, data science, applied AI, software engineering, or a directly relevant discipline.

Required Skills:

• Proven track record in developing and deploying machine learning models and AI-powered solutions.

• Strong command of data science methodologies, experimental design, model evaluation techniques, and statistical analysis.

• Hands-on software engineering proficiency, encompassing coding, debugging, comprehensive testing, and deployment practices.

• Demonstrated experience in building data-intensive applications, scalable machine learning systems, robust experimentation platforms, or AI-enabled products.

• Excellent programming skills with a strong ability to diagnose, troubleshoot, and resolve complex technical issues.

• Experience in evaluating, refining, and maintaining machine learning models, data pipelines, and AI applications throughout their lifecycle.

• High adaptability and a demonstrated ability to quickly learn new technologies, adjust to shifting priorities, and contribute effectively in ambiguous, fast-paced environments.

• Exceptional communication skills, with the proven ability to articulate complex technical concepts and findings clearly to both technical and non-technical audiences.

• Extensive experience working collaboratively and effectively across research, engineering, product management, and business teams. Preferred Skills:

• Experience in translating research concepts, academic publications, or emerging technologies into functional working prototypes and production-ready solutions.

• Deep familiarity and hands-on experience with foundation models, large language models (LLMs), generative AI systems, multimodal AI, agentic workflows, and retrieval-augmented generation (RAG) architectures.

• Proven ability to rapidly prototype and iterate on innovative ideas using modern AI development tools and AI-assisted coding workflows.

• Experience in designing and implementing comprehensive evaluation frameworks, benchmarks, and success metrics tailored for advanced AI systems.

• Practical experience with model fine-tuning, experimental validation, model validation processes, and performance optimization techniques.

• Prior experience working on research-driven initiatives or within innovation-focused environments.

• Demonstrated ability to ramp up quickly on new projects and deliver significant, tangible results within short project timelines.

• Experience supporting the end-to-end development of machine learning solutions, from initial experimentation through to deployment and validation.

• Proven flexibility and success in managing and contributing to multiple research or product domains concurrently.

• Availability for a long-term engagement, with a preference for candidates available for 12+ months.

📝 Enhancement Note: The requirement for 5-7+ years of experience in conjunction with preferred qualifications like LLMs, RAG, and agentic workflows suggests this role is targeted at senior or lead data scientists/ML engineers who can operate with a high degree of autonomy and contribute to cutting-edge AI product development. The emphasis on translating research points to a need for individuals who can bridge the gap between theoretical advancements and practical application.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of at least 2-3 significant machine learning or AI-driven projects that demonstrate your ability to translate research into functional prototypes or deployed solutions.

• Inclusion of detailed case studies for projects, highlighting the problem statement, your approach, the ML models or AI systems developed, and the measurable outcomes or insights derived.

• Evidence of experience in building and validating ML models, including details on data preprocessing, feature engineering, model selection, training, and evaluation methodologies.

• Documentation of your software engineering practices, such as code quality, testing strategies (unit, integration), and deployment considerations, particularly for AI/ML systems.

• Examples of your work in experimental design and execution, particularly how you validated hypotheses or benchmarked performance of AI models or systems. Process Documentation:

• Provide examples of how you have documented technical specifications, research findings, or system requirements for AI/ML projects, showcasing clarity and completeness.

• Demonstrate your approach to ML pipeline development and maintenance, including any version control, testing, or monitoring strategies employed.

• Showcase how you have defined and tracked success metrics for AI systems or ML models, illustrating your understanding of performance evaluation and impact measurement.

📝 Enhancement Note: Given the "Research Translation & Prototyping" focus, a portfolio is crucial. It should highlight not just the final models but the process of research validation, rapid iteration, and prototype development. Emphasis should be placed on projects that demonstrate the ability to quickly assess new technologies and build functional proof-of-concepts.

💵 Compensation & Benefits

Salary Range: $145,000 - $155,000 USD annually.

Benefits:

• Comprehensive Medical, Dental, and Vision Coverage: Ensuring holistic health and well-being for employees and their families.

• Flexible Spending Account (FSA): Offering pre-tax benefits for healthcare and dependent care expenses.

• 401k Program: Providing a retirement savings plan with potential employer matching contributions.

• Competitive Paid Time Off (PTO) Offerings: Generous leave for rest, rejuvenation, and personal time.

• Parental Leave: Supportive policies for new parents during this significant life event.

• Opportunities for Professional Growth and Development: Access to learning resources, training, and career advancement pathways.

Working Hours: Standard full-time (approximately 40 hours per week), with flexibility expected for project-driven deadlines and rapid prototyping cycles.

📝 Enhancement Note: The provided salary range of $145,000-$155,000 USD for a 5-7+ year experienced Machine Learning Data Scientist in Redmond, WA, aligns with industry benchmarks for senior technical roles in the Seattle metropolitan area, a major tech hub. The benefits package is standard for a technology solutions firm and includes key offerings expected by professionals in this field.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology Solutions / Consulting, with a strong focus on leveraging cutting-edge technology to drive business transformation and revenue generation. Blueprint Technologies operates at the intersection of strategy, business solutions, and product development, often working with clients to unlock value from existing assets through new technology applications.

Company Size: Blueprint Technologies is a medium-sized firm, indicated by its presence across the United States and a "bustling product division" and "multifaceted services team." This size often allows for a balance between structured processes and agile, innovative work environments.

Founded: Blueprint Technologies was founded with a mission to bridge the gap between strategy and execution using technology. While the exact founding date isn't provided, the company's description suggests a growth trajectory focused on innovation and client impact.

Team Structure:

• The Data Science and ML team likely comprises specialists in various AI domains, including research, applied ML, and engineering. They are expected to be highly collaborative.

• Reporting structure is likely to be within a tech leadership framework, with this role reporting to a lead scientist, engineering manager, or product lead, depending on the project focus.

• Cross-functional collaboration is a core tenet, with frequent interaction expected with researchers, software engineers, product managers, and potentially client-facing teams to ensure alignment and successful translation of research into viable solutions. Methodology:

• Data analysis and insights methods are central to validating research and prototypes, ensuring that development is grounded in empirical evidence and user feedback.

• Workflow planning and optimization strategies are critical for managing the rapid iteration cycles inherent in prototyping and research translation.

• Automation and efficiency practices are likely employed in building ML pipelines, testing frameworks, and development workflows to accelerate the pace of innovation.

Company Website: https://www.bpcs.com/

📝 Enhancement Note: The company description emphasizes "solving complicated problems," "leveraging cutting-edge technology," and "transforming and growing companies." This suggests a culture that values innovation, technical expertise, and a proactive, results-oriented approach, which aligns well with the requirements of an ML Data Scientist focused on research translation and prototyping.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a senior or lead individual contributor within the Machine Learning and Data Science domain. It requires a strong foundation in both theoretical research translation and practical software engineering for prototyping. The 5-7+ years of experience requirement signifies a level where candidates are expected to drive projects, mentor junior team members, and contribute to strategic technical direction.

Reporting Structure: The candidate will likely report to a Director or Senior Manager of Data Science, AI Research, or Product Engineering. They will be part of a team that collaborates closely with product management and other engineering disciplines.

Operations Impact: The impact of this role is significant, directly influencing the company's ability to innovate and capitalize on emerging AI trends. By translating research into prototypes, this position helps Blueprint Technologies identify and develop new revenue streams and business lines, enhancing its competitive edge and client offerings.

Growth Opportunities:

• Specialization: Deepen expertise in specific AI domains like generative AI, multimodal systems, or agentic workflows, potentially leading to Principal Scientist or Architect roles.

• Leadership: Transition into technical leadership roles, such as a Machine Learning Engineering Lead or AI Research Manager, overseeing teams and strategic initiatives.

• Product Development: Move into product-focused roles, leveraging deep technical understanding to guide the development of AI-powered products from concept to market.

• Consulting Expertise: Develop advanced client-facing consulting skills, advising businesses on AI strategy and implementation, building on the research translation experience.

📝 Enhancement Note: The "Research Translation & Prototyping" aspect of the role suggests a strong emphasis on innovation and future-facing technologies. Career growth will likely involve deepening expertise in these advanced AI areas and potentially moving into leadership roles that drive these initiatives within Blueprint Technologies.

🌐 Work Environment

Office Type: Blueprint Technologies has a hybrid work model, with a headquarters in Bellevue, Washington, and a presence across the US. The role is advertised as "Remote OK" with a primary focus on Washington State, suggesting that while remote work is accommodated, proximity to the Bellevue/Redmond tech corridor is preferred for potential collaboration or team events.

Office Location(s): Headquarters in Bellevue, Washington, with other offices across the United States. Specific remote work expectations may involve occasional travel to client sites or company offices for key meetings or collaborative sessions.

Workspace Context:

• The environment is described as "agile" and "fast-paced," typical of technology consulting and product development firms.

• Expect a dynamic workspace that encourages rapid iteration, experimentation, and problem-solving, often in ambiguous situations.

• Access to modern AI development tools, cloud computing resources, and potentially AI-assisted coding workflows would be standard.

• Opportunities for continuous learning and interaction with highly skilled peers in data science, ML engineering, and product development are abundant.

Work Schedule: A standard full-time schedule (around 40 hours per week) is expected, but the nature of research translation and prototyping often requires flexibility to meet project milestones and respond to urgent research findings or development needs.

📝 Enhancement Note: The "Remote OK" status, coupled with the Redmond, WA location, implies that while the role can be performed remotely, there might be an expectation for occasional in-person collaboration, especially if the candidate is within the Seattle metropolitan area. This hybrid approach is common for roles requiring deep technical collaboration.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A brief call with a recruiter to assess basic qualifications, interest, and cultural fit.

• Technical Interview(s): In-depth discussions focusing on ML concepts, algorithms, software engineering best practices, and problem-solving approaches. Expect coding challenges and scenario-based questions related to research translation and prototyping.

• Portfolio Review: A dedicated session where you will present your past projects, highlighting your contributions to research translation, prototyping, and ML model development. Be prepared to discuss your design choices, challenges, and outcomes in detail.

• Cross-functional/Manager Interview: A conversation with the hiring manager and potentially team members from research, engineering, or product to evaluate your collaboration skills, strategic thinking, and alignment with the team's objectives.

• Final Round: May involve a presentation or a more strategic discussion about how you would approach specific challenges within Blueprint Technologies.

Portfolio Review Tips:

• Showcase Research Translation: Include projects where you took novel research or experimental concepts and turned them into functional prototypes or proof-of-concepts.

• Highlight Prototyping Skills: Demonstrate your ability to rapidly build and iterate on solutions, showcasing the tools and methodologies used.

• Quantify Impact: Wherever possible, use metrics to show the effectiveness of your prototypes or the insights gained from your experiments (e.g., performance improvements, validation success rates, potential ROI).

• Technical Depth: Be prepared to dive deep into the technical details of your ML models, software architecture, and experimental setups.

• Storytelling: Frame your project presentations as a narrative, explaining the problem, your solution, and the results, emphasizing your unique contributions.

Challenge Preparation:

• Practice coding problems focused on data manipulation, algorithm implementation, and ML model building.

• Prepare to discuss your approach to evaluating new AI technologies and translating research findings into actionable prototypes.

• Be ready to articulate your understanding of current AI trends (LLMs, Generative AI, Multimodal AI) and how they can be applied.

• Develop clear explanations for complex technical concepts, as you will need to communicate with both technical and non-technical stakeholders.

• Familiarize yourself with Blueprint Technologies' work and how AI/ML fits into their client solutions and product offerings.

📝 Enhancement Note: The emphasis on "Research Translation & Prototyping" means the interview and portfolio review will heavily scrutinize your ability to bridge the gap between abstract research and concrete, working examples. Be ready to demonstrate not just theoretical knowledge but practical application and rapid development skills.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary for ML/Data Science), potentially others like C++, Java, or Go for production systems.

• ML Frameworks: TensorFlow, PyTorch, scikit-learn.

• AI/LLM Libraries: Hugging Face Transformers, LangChain, LlamaIndex, potentially libraries for specific generative AI tasks.

• Cloud Platforms: AWS, Azure, or GCP for model training, deployment, and data storage.

• Containerization: Docker, Kubernetes for deployment and scalability.

Analytics & Reporting:

• Data Analysis Libraries: Pandas, NumPy.

• Visualization Tools: Matplotlib, Seaborn, potentially Tableau or Power BI for broader stakeholder reporting.

• Experiment Tracking: MLflow, Weights & Biases, or similar platforms for managing experiments and model versions.

CRM & Automation: (Less relevant for this specific role, but general company context might involve)

• Version Control: Git, GitHub/GitLab/Bitbucket.

• CI/CD Tools: Jenkins, GitLab CI, GitHub Actions for automated testing and deployment.

• Data Warehousing/Lakes: Snowflake, BigQuery, Redshift, S3, Azure Data Lake Storage.

📝 Enhancement Note: The "Preferred Qualifications" list specific modern AI technologies like LLMs, RAG, and agentic workflows. Candidates should be prepared to discuss their experience with these and related tools/libraries. Proficiency in Python and core ML frameworks is essential.

👥 Team Culture & Values

Operations Values:

• Innovation & Exploration: A strong drive to explore and experiment with new AI research and technologies, fostering a culture of continuous learning and pushing boundaries.

• Impact & Measurability: A focus on delivering tangible results and measurable outcomes, ensuring that research translation and prototyping efforts contribute to business value and strategic goals.

• Collaboration & Cross-Functionality: A commitment to working seamlessly with diverse teams—researchers, engineers, product managers—to achieve shared objectives.

• Agility & Adaptability: The ability to thrive in fast-paced, often ambiguous environments, quickly adapting to new information, technologies, and project priorities.

• Technical Excellence: A dedication to high-quality engineering and robust ML practices, ensuring that prototypes are well-built and their potential for production is clearly understood.

Collaboration Style:

• Open Communication: Encouraging direct and clear communication channels between team members and cross-functional partners to facilitate rapid feedback loops and problem-solving.

• Iterative Development: A process-oriented approach that involves frequent cycles of building, testing, and refining prototypes based on experimental results and stakeholder feedback.

• Knowledge Sharing: A culture that promotes the sharing of insights, learnings, and best practices across the team and broader organization, fostering collective growth and innovation.

📝 Enhancement Note: The company's emphasis on solving "complicated problems" and "unlocking value from existing assets" suggests a culture that values deep technical expertise combined with a pragmatic, business-oriented application of that knowledge. The "bold, smart, agile, and fun" descriptor hints at a dynamic and engaging work environment.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging the Research-Development Gap: Effectively translating complex, often theoretical AI research findings into practical, demonstrable prototypes within aggressive timelines.

• Navigating Ambiguity: Working in a rapidly evolving field where technologies and best practices are constantly changing, requiring continuous learning and adaptation.

• Rapid Prototyping & Validation: The pressure to quickly build and validate new ideas, often with incomplete data or under-specified requirements, requires strong problem-solving and resourcefulness.

• Cross-functional Alignment: Ensuring seamless communication and alignment across diverse teams (research, engineering, product) with potentially different priorities and technical understandings.

• Staying Ahead of the Curve: Keeping pace with the exponential advancements in AI, particularly in areas like generative AI and foundation models, to identify and leverage the most impactful opportunities.

Learning & Development Opportunities:

• Cutting-Edge AI Exposure: Direct engagement with the latest advancements in ML, generative AI, multimodal systems, and agentic workflows.

• Advanced Technical Skill Development: Opportunities to deepen expertise in specific ML techniques, AI architectures, and software engineering best practices for AI systems.

• Mentorship: Access to experienced researchers and engineers who can provide guidance on technical challenges and career development.

• Industry Exposure: Potential to work on diverse client projects or internal product initiatives, broadening exposure to different business problems and technological applications.

• Innovation Labs/Hackathons: Participation in internal innovation events or hackathons focused on exploring new AI applications and rapid prototyping.

📝 Enhancement Note: The "Research Translation & Prototyping" nature of the role inherently presents challenges related to innovation, rapid iteration, and staying at the forefront of AI. The growth opportunities are directly tied to mastering these challenges and deepening expertise in advanced AI domains.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you translated a complex research concept or algorithm into a functional prototype. What were the key challenges, and how did you overcome them?" (Focus on your process, technical choices, and outcomes.)

• "How would you approach evaluating the potential of a new foundation model for a specific business application? What metrics would you use?" (Demonstrate your understanding of evaluation frameworks and practical application.)

• "Walk me through your process for designing and executing an experiment to validate a new AI model's performance. What are the critical factors you consider?" (Highlight your scientific rigor and experimental design skills.)

• "Imagine you need to build a rapid prototype for a generative AI application. What are the first steps you would take, and what tools would you prioritize?" (Showcase your pragmatic prototyping approach and tool familiarity.) Company & Culture Questions:

• "Based on Blueprint's mission, how do you see your role in translating AI research contributing to unlocking new revenue streams or business lines for clients?" (Connect your skills to the company's strategic goals.)

• "How do you stay current with the rapidly evolving landscape of AI research and development? Can you give an example of a recent advancement you've explored?" (Demonstrate your passion for continuous learning and industry awareness.)

• "Describe your experience working in agile or fast-paced environments with ambiguous requirements. How do you ensure progress and deliver results?" (Highlight your adaptability and problem-solving skills in dynamic settings.) Portfolio Presentation Strategy:

• Structure for Impact: Organize your portfolio around key projects that best exemplify your research translation and prototyping capabilities. For each project, clearly articulate: The Research/Problem, Your Approach/Methodology, The Prototype/Solution, The Key Findings/Results, and Your Specific Contributions.

• Quantify Achievements: Use metrics liberally to demonstrate the success of your prototypes or the insights gained. This could include performance benchmarks, user engagement data, or potential business impact.

• Technical Deep Dive: Be ready to discuss the technical intricacies of your models, algorithms, and systems. Explain your design choices and justify why you selected specific tools or approaches.

• Focus on "Translation": Emphasize how you moved from a research idea to a tangible, working demonstration, highlighting the iterative process and any challenges overcome.

• Engage Your Audience: Practice your presentation to be clear, concise, and engaging. Be prepared for questions that probe deeper into your decision-making and technical expertise.

📝 Enhancement Note: Given the role's specialized nature, interviewers will be looking for candidates who can demonstrate a clear understanding of the AI research lifecycle and a proven ability to execute on translating that research into tangible outcomes through rapid prototyping.

📌 Application Steps

To apply for this Machine Learning Data Scientist position:

• Submit your application through the Greenhouse application link provided.

• Curate Your Portfolio: Select 2-3 of your most impactful projects that best demonstrate your experience in research translation and rapid ML prototyping. Ensure these examples clearly articulate your role, the technical challenges, your solutions, and measurable outcomes.

• Tailor Your Resume: Highlight keywords and experiences directly relevant to machine learning, data science, applied AI, software engineering, prototyping, and specific AI technologies mentioned (LLMs, Generative AI, RAG). Quantify your achievements wherever possible.

• Prepare Your Presentation: Practice presenting your portfolio projects. Be ready to discuss your methodology, technical decisions, and the impact of your work in detail, anticipating questions about research translation and validation processes.

• Research Blueprint Technologies: Understand their mission, their approach to technology solutions, and how they leverage AI and ML. Prepare to discuss how your skills and experience align with their strategic goals and company culture.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.