Software Engineer, AI Research & Prototyping

📍 Job Overview

Job Title: Software Engineer, AI Research & Prototyping

Company: Sage Care Inc

Location: Palo Alto, California, United States

Job Type: FULL_TIME

Category: AI/Machine Learning Engineering, Software Engineering

Date Posted: 2026-07-29

Experience Level: Mid-Level (3-5 years)

Remote Status: Hybrid

🚀 Role Summary

• This role focuses on bridging the gap between cutting-edge AI research and practical application within a fast-growing healthcare startup, specifically in voice AI and LLM technologies.

• You will be responsible for identifying, experimenting with, and prototyping new AI techniques to enhance the company's care navigation platform, leveraging real production data.

• A key aspect of this position involves knowledge transfer and education, ensuring the broader engineering team benefits from your investigations through clear documentation, benchmarks, and technical deep-dives.

• The role requires a blend of strong software engineering skills, hands-on machine learning experience with LLMs, and a rigorous experimental mindset to drive evidence-based decision-making.

📝 Enhancement Note: While the job title is "Software Engineer, AI Research & Prototyping," the description strongly emphasizes applied ML and experimental rigor, positioning this role as a bridge between pure research and production engineering. The focus on "prototypes fast, measures honestly, and makes the people around them smarter" suggests a critical, analytical function within the GTM strategy for AI product development.

📈 Primary Responsibilities

• Continuously monitor and critically evaluate emerging techniques in voice AI, Large Language Models (LLMs), and agent systems to identify those relevant to Sage Care's strategic objectives.

• Design and implement robust, structured experiments with clear controls and baselines to rigorously test hypotheses and differentiate signal from noise in AI model performance.

• Develop rapid, end-to-end prototypes using Python to test promising AI concepts and evaluate their effectiveness against real conversation data.

• Conduct head-to-head comparative evaluations of various AI models, service providers, and methodologies, including reasoning approaches, speech models (STT/TTS), and orchestration patterns.

• Translate every investigation into a tangible, team-usable artifact, such as detailed benchmarks, comprehensive technical deep-dives, engaging tech talks, or well-supported recommendations backed by empirical evidence.

• Collaborate closely with platform engineers to ensure seamless hand-off of validated AI prototypes and research findings for production implementation.

• Contribute to building and maintaining an internal knowledge base that articulates the 'how' and 'why' behind the company's AI stack, fostering organizational learning.

• Proactively identify and articulate the business impact of adopted AI techniques, linking technical advancements to improved patient outcomes and economic efficiencies.

📝 Enhancement Note: The responsibilities highlight a proactive, investigative, and educational function. The emphasis on "teaching" and creating "team-usable artifacts" indicates a need for strong communication and documentation skills, crucial for operationalizing AI advancements within the GTM.

🎓 Skills & Qualifications

Education:

• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or a related field, or equivalent research experience (Preferred).

• Equivalent practical experience in lieu of a formal degree will be considered. Experience:

• 3+ years of combined software engineering and applied Machine Learning (ML) experience.

• Proven track record of taking prototypes from concept through to production-level validation, ideally in collaboration with engineering teams.

• Experience evaluating AI systems in high-stakes domains such as healthcare is a significant advantage. Required Skills:

• Python Proficiency: Strong command of Python for building and executing end-to-end experiments without reliance on dedicated infrastructure support.

• LLM Expertise: Hands-on experience with Large Language Models (LLMs), including effective prompting techniques, robust evaluation methodologies, and an intuitive understanding of how model behavior varies across different techniques and providers.

• Experimental Rigor: Demonstrated experience in designing and conducting tests with appropriate controls, establishing baselines, and performing honest, unbiased measurement of results.

• Knowledge Transfer & Teaching: A verifiable track record of teaching or transferring knowledge, which can manifest as teaching/TA experience, leading workshops, technical writing, delivering internal tech talks, contributing to well-documented open-source projects, or engaging in developer education.

• Intellectual Honesty: A commitment to objective reporting, including the ability to candidly communicate when promising ideas do not yield the expected results.

• Software Engineering Fundamentals: Solid understanding of software development principles and best practices.

Preferred Skills:

• Voice/Speech Systems: Experience with components of voice AI, including Speech-to-Text (STT), Text-to-Speech (TTS), and real-time voice processing pipelines.

• Research & Publications: Evidence of research contributions through academic publications, technical blog posts, or active participation in open-source projects.

• AI Systems Evaluation: Experience in evaluating AI systems specifically within the healthcare sector or other critical, high-impact domains.

• Agent Systems: Familiarity with the design and implementation of AI agent systems and orchestration patterns.

📝 Enhancement Note: The "3+ years of software engineering or applied ML experience" combined with the emphasis on experimental rigor and knowledge transfer suggests this role is geared towards an individual contributor who can operate with significant autonomy but also influence team direction. The preferred skills indicate a desire for someone who can hit the ground running with advanced AI concepts.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Experimental Design Case Studies: Showcase examples of experiments designed to test hypotheses, detailing the hypothesis, experimental setup, controls, metrics, and analyzed results.

• Prototyping Demonstrations: Provide evidence of rapid prototyping capabilities, ideally with code repositories (e.g., GitHub) demonstrating end-to-end functionality of AI models or features.

• Knowledge Transfer Artifacts: Include examples of technical writing, documentation, presentations (e.g., slides from tech talks), or any other materials used to educate others on complex technical topics.

• Performance Benchmarking: Demonstrate experience in benchmarking AI models or systems, showing how performance was measured, compared, and reported.

Process Documentation:

• Experimentation Workflow: Document your typical workflow for designing, executing, and analyzing AI experiments, emphasizing iterative improvement and data-driven decision-making.

• Prototyping Lifecycle: Outline your approach to prototyping, from initial concept and feasibility assessment to testing and integration readiness.

• Knowledge Dissemination Process: Detail how you approach sharing technical insights and research findings with cross-functional teams to ensure adoption and understanding.

📝 Enhancement Note: Given the role's focus on "prototyping fast, measures honestly, and makes the people around them smarter," a portfolio that clearly demonstrates these capabilities is crucial. This includes showcasing the ability to design rigorous experiments, build functional prototypes, and effectively communicate complex technical findings. The emphasis is on applied impact and team enablement rather than pure theoretical research.

💵 Compensation & Benefits

Salary Range: $160,000 - $200,000 USD per year.

Benefits:

• Stock Options: Opportunity to own a stake in a rapidly growing early-stage company.

• Comprehensive Health Insurance: Medical, dental, and vision coverage.

• Paid Time Off: Generous vacation, sick leave, and holidays.

• Professional Development: Support for continuous learning, conferences, and training.

• 401(k) Plan: Retirement savings plan with potential company match.

• Remote Work Stipend: For home office setup and connectivity.

Working Hours:

• Standard full-time hours (approximately 40 hours per week).

• Flexibility may be offered, but the role requires active participation in team discussions and collaboration, especially during core business hours for effective knowledge transfer and prototyping efforts.

📝 Enhancement Note: The salary range provided is competitive for a Software Engineer with specialized AI/ML experience in the Palo Alto area. The inclusion of stock options is typical for early-stage startups and represents significant potential upside.

🎯 Team & Company Context

🏢 Company Culture

Industry: Healthcare Technology (HealthTech) / AI in Healthcare. Sage Care is transforming healthcare by simplifying care navigation, aiming to improve patient outcomes and economic efficiencies through AI.

Company Size: Early-stage startup, fast-growing. This implies a dynamic, agile environment where individual contributions have a significant impact.

Founded: Founded by experienced leaders from prominent tech and healthcare companies, indicating a strong foundation and strategic vision.

Team Structure:

• The AI Research & Prototyping team is likely small and highly specialized, working closely with core platform engineering and product teams.

• Reporting structure is likely direct to an AI/Engineering Lead or CTO, emphasizing autonomy and direct impact.

• Cross-functional collaboration is essential, involving close work with product managers, data scientists, and backend engineers to validate and integrate AI solutions. Methodology:

• Data-Driven Decision Making: Strong emphasis on evidence-based approaches, utilizing real production data for experimentation and evaluation.

• Agile Prototyping & Iteration: Focus on rapid development and testing of new ideas to quickly validate or invalidate hypotheses.

• Knowledge Sharing & Education: A culture that values and promotes the dissemination of technical knowledge across the engineering organization.

Company Website: https://www.sage.care/

📝 Enhancement Note: As an early-stage startup, Sage Care likely fosters a culture of innovation, speed, and ownership. The AI focus suggests a technically driven environment where data and experimentation are paramount. The healthcare domain adds a layer of mission-driven purpose.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Mid-Level Software Engineer with a specialization in AI Research and Prototyping. It requires a solid foundation in software engineering and applied ML, with the capacity for independent work and knowledge dissemination.

Reporting Structure: The role likely reports to a senior technical leader, such as the Head of AI, CTO, or a Lead AI Engineer. This structure allows for direct mentorship and exposure to high-level strategic discussions regarding AI product development.

Operations Impact: This role is critical for the company's GTM strategy for AI-powered features. By identifying and validating novel AI techniques, the engineer directly influences the product's competitive edge, efficiency, and ability to deliver on its core promise of simplified care navigation. Their work ensures that Sage Care leverages the most effective AI solutions, leading to better patient experiences and stronger business outcomes.

Growth Opportunities:

• Specialization Deepening: Opportunity to become a subject matter expert in specific areas of voice AI, LLMs, or agent systems.

• Technical Leadership: Potential to grow into a lead role within the AI team, mentoring junior engineers and defining technical roadmaps.

• Product Influence: Direct impact on product strategy and feature development through validated AI prototypes.

• Cross-functional Mobility: Possibility to move into more production-focused engineering roles or even product management roles leveraging deep AI understanding.

• Industry Expertise: Develop specialized knowledge in AI applications within the healthcare sector, a high-growth and high-impact field.

📝 Enhancement Note: The "success looks like" section provides clear indicators of growth: becoming the primary source of AI technique knowledge, enabling faster and more confident AI decisions, and ensuring validated prototypes are production-ready. This points towards a trajectory of increasing influence and technical leadership.

🌐 Work Environment

Office Type: Hybrid work environment. This suggests a blend of remote work flexibility and in-office collaboration.

Office Location(s): Palo Alto, California. This location places Sage Care within a major technology hub, offering access to talent and resources.

Workspace Context:

• Collaborative Space: The hybrid model implies that in-office days will be focused on team collaboration, brainstorming, and knowledge sharing sessions, likely in a modern, open-plan office environment designed to foster innovation.

• Tools & Technology: Access to robust computing resources, cloud platforms, and development tools necessary for AI research and prototyping.

• Team Interaction: Opportunities for direct interaction with peers, senior leadership, and cross-functional teams during in-office days, crucial for effective knowledge transfer and rapid feedback loops.

Work Schedule:

• While the role is full-time, the hybrid nature and focus on research/prototyping suggest a degree of flexibility in daily scheduling, allowing individuals to optimize their work for deep focus and collaborative periods. Core hours will likely be established for team syncs and critical meetings.

📝 Enhancement Note: The hybrid model in Palo Alto, a high-cost-of-living area, suggests that Sage Care is committed to attracting top talent by offering flexibility while maintaining the benefits of in-person collaboration for critical R&D functions.

📄 Application & Portfolio Review Process

Interview Process:

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

• Technical Interview 1 (Coding & ML Fundamentals): Focus on Python programming skills, data structures, algorithms, and core machine learning concepts. Expect practical coding challenges.

• Technical Interview 2 (AI Research & Experimentation): Deep dive into LLMs, experimental design, voice AI concepts, and your approach to evaluating AI models. This may involve discussing past projects and hypothetical scenarios.

• Portfolio Review & Presentation: A session where you present 1-2 key projects from your portfolio, demonstrating your prototyping, experimentation, and knowledge transfer capabilities. Be prepared to discuss your process, challenges, and outcomes.

• Hiring Manager/Team Interview: Discussion about team dynamics, collaboration style, career aspirations, and how you would contribute to Sage Care's mission.

• Final Round (Potential): May involve a discussion with senior leadership or a more complex problem-solving exercise.

Portfolio Review Tips:

• Highlight Impact: For each project, clearly articulate the problem statement, your role, the methodology used, the results achieved (quantified where possible), and the impact on the team or product.

• Showcase Process: Demonstrate your thought process for experimental design, data handling, model selection, and evaluation. Explain why you made certain choices.

• Emphasize Knowledge Transfer: Include examples of documentation, presentations, or code that clearly show how you made complex AI concepts accessible to others.

• Tailor to Sage Care: Connect your portfolio projects to Sage Care's mission and the specific challenges of voice AI in healthcare.

• Prepare for Questions: Anticipate questions about your experimental rigor, how you handle unexpected results, and how you prioritize research efforts.

Challenge Preparation:

• Coding Challenges: Practice Python coding problems, focusing on efficiency and clarity.

• ML Scenario Questions: Prepare to discuss how you would approach evaluating a new LLM for a specific task, designing an A/B test for a voice feature, or debugging an AI model's performance.

• Presentation Practice: Rehearse your portfolio presentation to ensure it is concise, impactful, and within the allotted time. Practice explaining technical details without over-complicating.

📝 Enhancement Note: The emphasis on a portfolio review and knowledge transfer suggests that Sage Care values not just technical execution but also the ability to translate complex AI research into actionable insights for the broader team, a critical component of operationalizing AI in a product context.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python is the primary language for research, prototyping, and experimentation.

• AI/ML Frameworks: Libraries such as TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers for model development and experimentation.

• LLM Interaction Libraries: LangChain, LlamaIndex, or similar frameworks for building LLM-powered applications and agents.

• Cloud Platforms: AWS, GCP, or Azure for compute, storage, and ML services (e.g., SageMaker, Vertex AI).

Analytics & Reporting:

• Data Analysis Libraries: Pandas, NumPy for data manipulation and analysis.

• Visualization Tools: Matplotlib, Seaborn, Plotly for creating charts and graphs to support experiment findings.

• Experiment Tracking: Tools like MLflow, Weights & Biases, or internal logging mechanisms for managing and analyzing experiment results.

CRM & Automation:

• Version Control: Git (GitHub, GitLab, Bitbucket) for code management and collaboration.

• Containerization: Docker for creating reproducible development and testing environments.

• CI/CD: Tools for automating build, test, and deployment pipelines (though less emphasis for pure prototyping, important for hand-offs).

📝 Enhancement Note: The emphasis on Python, LLM interaction libraries, and cloud platforms indicates a modern, cloud-native development environment focused on rapid experimentation and data-driven validation. The specific mention of "real production data" highlights the need for robust data handling and privacy considerations.

👥 Team Culture & Values

Operations Values:

• Intellectual Honesty: A commitment to unbiased, evidence-based decision-making, even when results challenge initial hypotheses.

• Collaboration & Knowledge Sharing: Valuing open communication and actively teaching/learning from peers to elevate the entire team's capabilities.

• Impact-Driven Innovation: Focusing research and prototyping efforts on solutions that deliver tangible improvements to patient care and business outcomes.

• Agility & Speed: Embracing rapid iteration and prototyping to quickly validate ideas and adapt to the fast-paced evolution of AI.

• Curiosity & Continuous Learning: A drive to stay abreast of the latest advancements in AI research and apply them practically.

Collaboration Style:

• Cross-Functional Integration: Working closely with product, engineering, and potentially clinical teams to ensure AI research aligns with business needs and is technically feasible for production.

• Open Feedback Loops: Encouraging constructive feedback on experimental designs, prototypes, and findings to refine approaches.

• Knowledge Dissemination Focus: Proactively sharing insights through documentation, internal talks, and code reviews to foster a culture of collective learning and operational excellence in AI.

📝 Enhancement Note: The values highlight a culture that is both technically rigorous and highly collaborative, with a clear mission focus on improving healthcare through AI. The emphasis on "making people around them smarter" is a key cultural indicator.

⚡ Challenges & Growth Opportunities

Challenges:

• Pace of AI Evolution: Keeping up with the rapid advancements in voice AI, LLMs, and related fields requires continuous learning and rapid adaptation.

• Bridging Research and Production: Translating promising research ideas into robust, scalable production systems can be complex, requiring strong collaboration with platform engineers.

• Experimental Rigor with Real Data: Ensuring experimental integrity when working with potentially noisy or complex real-world healthcare data.

• Communicating Complex Ideas: Effectively explaining intricate AI concepts and experimental results to diverse audiences, including non-technical stakeholders.

Learning & Development Opportunities:

• Deep Dive into Cutting-Edge AI: Direct exposure to and experimentation with state-of-the-art AI models and techniques.

• Healthcare AI Specialization: Developing expertise in the unique challenges and opportunities of applying AI within the healthcare domain.

• Mentorship: Learning from experienced leaders in AI, software engineering, and healthcare technology.

• Industry Exposure: Potential to attend conferences or engage with the broader AI research community.

• Skill Development: Opportunities to enhance skills in experimental design, data analysis, Python programming, and technical communication.

📝 Enhancement Note: The challenges are inherent to working at the forefront of AI in a dynamic startup environment. The growth opportunities are significant, offering deep technical specialization and the chance to influence a growing company's AI strategy.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to evaluate a new AI technique. What was your process, what were the results, and how did you communicate them?" (Focus on experimental design, honest reporting, and knowledge transfer).

• "How would you design an experiment to test if a new LLM prompting strategy improves patient engagement in our care navigation app?" (Demonstrate understanding of controls, metrics, and practical application).

• "Imagine a promising AI prototype you built wasn't performing as expected in initial tests. How would you diagnose the issue and decide whether to iterate or abandon the idea?" (Highlight intellectual honesty, analytical skills, and iterative process). Company & Culture Questions:

• "Why are you interested in applying AI to healthcare, and specifically at Sage Care?" (Connect your motivations to the company's mission and the role's impact).

• "How do you approach teaching or sharing technical knowledge with your colleagues?" (Provide specific examples of your knowledge transfer methods).

• "Describe a time you had to collaborate with engineers from different disciplines (e.g., backend, product). How did you ensure alignment and effective communication?" (Showcase collaboration skills). Portfolio Presentation Strategy:

• Structure is Key: For each project, follow a clear narrative: Problem -> Your Approach (Experiment Design, Prototyping) -> Results (Quantified Data) -> Impact/Learning -> Future Steps/Hand-off.

• Show, Don't Just Tell: If possible, include brief code snippets or visuals that illustrate your work. For prototypes, explain the core functionality and how it was tested.

• Focus on the 'Why': Be prepared to explain the rationale behind your technical decisions, experimental setups, and evaluation metrics.

• Quantify Impact: Whenever possible, use numbers and data to demonstrate the effectiveness of your prototypes or the insights gained from experiments.

• Practice Your Story: Rehearse your presentation to ensure it flows logically, stays within time limits, and clearly conveys your skills and contributions.

📝 Enhancement Note: Preparation should focus on demonstrating not just technical proficiency but also the ability to think critically, experiment rigorously, and effectively communicate complex AI concepts and findings to enable broader team adoption and strategic decision-making.

📌 Application Steps

To apply for this Software Engineer, AI Research & Prototyping position:

• Submit your application through the provided Ashby link.

• Tailor Your Resume: Highlight your Python proficiency, hands-on LLM experience, experimental design skills, and any instances of teaching or knowledge transfer. Use keywords from the job description.

• Curate Your Portfolio: Select 1-2 projects that best showcase your ability to prototype AI solutions and conduct rigorous experiments. Ensure your portfolio clearly demonstrates your process and impact.

• Prepare Your Narrative: Practice explaining your portfolio projects, focusing on the "what, why, and how" of your work, and be ready to articulate how your skills align with Sage Care's mission and the role's responsibilities.

• Research Sage Care: Understand their mission, their approach to AI in healthcare, and their current stage of growth. This will help you tailor your answers and demonstrate genuine interest.

⚠️ 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.