Senior Product Designer, AI

📍 Job Overview

Job Title: Senior Product Designer, AI

Company: Sigma Computing

Location: San Francisco, CA

Job Type: Full-time

Category: Product Design / AI Engineering

Date Posted: 2026-05-25T04:16:44

Experience Level: 5-10 years

Remote Status: Hybrid (4 days on-site)

🚀 Role Summary

• This unique role blends senior-level product design with hands-on AI engineering, focusing on building intelligent applications and interfaces powered by AI.

• Candidates will leverage AI tools, including LLMs and AI coding assistants, to accelerate the design and development lifecycle, from ideation and specification to implementation and iteration.

• The position demands innovation in designing novel interaction paradigms for conversational AI, particularly within data-intensive contexts where precision and trust are paramount.

• A key aspect involves developing the underlying AI scaffolding such as evaluation frameworks, reusable skills, and tooling to ensure AI systems behave predictably and improve over time.

📝 Enhancement Note: This role is distinctly positioned at the intersection of design and AI engineering, moving beyond traditional UI/UX to explore and define "AI-native UX." The emphasis on "shipping faster with AI," "building skills and evals," and "inventing new interaction paradigms" signifies a forward-thinking approach to product development where AI is not just a feature, but a core component of the design and engineering process. The requirement for on-site presence four days a week suggests a collaborative, in-person environment crucial for rapid iteration and cross-functional alignment.

📈 Primary Responsibilities

• AI-Driven Design & Development: Utilize Large Language Models (LLMs) for scope clarification, spec drafting, edge case identification, and team alignment. Employ AI coding assistants (e.g., Cursor, Claude Code) for rapid prototyping and iteration of interfaces, guiding structure, behavior, motion, and UX quality.

• Production Deployment & Refinement: Directly contribute to production code by fixing small interaction and refinement issues, and collaborate with engineering to determine which AI-generated features and prototypes are ready for product integration.

• Novel AI Interaction Design: Invent, prototype, and validate new interaction patterns for conversational interfaces, focusing on how users converse with, direct, and trust AI systems, especially in data contexts requiring high precision and confidence.

• AI Reliability & Scaffolding: Develop and implement evaluation frameworks, reusable skills, and specialized tooling to enhance the reliability, predictability, and performance of Sigma's AI features.

• AI Model Behavior Shaping: Iterate on evaluation metrics and training data to improve AI performance in generating design outputs, effectively "teaching" AI to function as a proficient designer.

• Craft & Best Practice Leadership: Establish and champion the standards for exceptional AI-native UX at Sigma, defining patterns and principles that will guide the broader design and engineering teams.

• Cross-Functional Collaboration: Partner closely with Product Managers, Engineers, ML Researchers, and Executives to communicate design decisions, technical feasibility, and AI performance insights effectively.

📝 Enhancement Note: The responsibilities highlight a hands-on approach that blends strategic design thinking with practical engineering execution. The expectation to "merge code to prod when fits" and "fix small interaction and refinement issues directly on prod code" indicates a "full-stack" design role, common in fast-paced, AI-centric startups, where designers are empowered to directly impact the product. Defining "what great AI-native UX looks like" signifies a leadership component where the successful candidate will set the vision for future product development.

🎓 Skills & Qualifications

Education: While no specific degree is mandated, a strong foundation in Computer Science, Human-Computer Interaction (HCI), Design, or a related field is implied by the technical demands of the role.

Experience: 4+ years in interaction design or design engineering, with a significant portion involving recent, hands-on experience with AI tooling and methodologies.

Required Skills:

• AI Tooling Proficiency: Demonstrated, hands-on experience using AI coding assistants (e.g., Cursor, Claude Code) to ship real product features or substantial side projects. Ability to articulate how AI accelerated development and where human intervention was critical.

• AI System Development: Experience in writing evaluations (evals), agents, prompt libraries, or skills for AI systems. Understanding of the challenges and methodologies involved in AI product development, particularly concerning evaluation.

• Interaction Design Fundamentals: Strong grasp of core interaction design principles, with the ability to articulate and defend design choices regarding new patterns versus extending existing ones.

• AI UX Expertise: Well-formed opinions and practical experience addressing key AI UX challenges such as trust signals, error recovery, and progressive disclosure of reasoning.

• Communication & Collaboration: Exceptional ability to communicate complex design and technical concepts effectively to diverse stakeholders including PMs, engineers, ML researchers, and executives.

• System-Wide Thinking: Demonstrated ability to think holistically about product systems, considering the interplay between AI components, user interfaces, and business objectives.

Preferred Skills:

• Conversational Interface Design: Experience specifically designing and validating conversational AI interfaces, particularly in data-rich environments.

• Prototyping in Code: Proven ability to build functional prototypes using code, demonstrating a strong bridge between design and engineering.

• AI Model Behavior Tuning: Experience in iterating on AI evaluations and training data to directly influence and improve AI performance.

• Figma Proficiency: Familiarity with industry-standard design tools for visual design and prototyping.

📝 Enhancement Note: The emphasis on "not just prompting, but building" with AI coding assistants is a critical differentiator. Candidates should be prepared to showcase tangible examples of how they've integrated AI into their workflow and product development process, demonstrating practical application beyond theoretical knowledge. The portfolio requirement specifically calls for AI-first product work, indicating this is a non-negotiable aspect.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Released Products: Showcase designs that have been successfully released to production, demonstrating system-wide thinking and breadth/depth in interactive design.

• AI-First Approach: Include projects where AI was a primary consideration or driver of the design, illustrating an "AI-first" product development methodology.

• Conversational Interfaces: Bonus points for projects involving intricate interaction problems transformed into elegant flows, especially if they include conversational interfaces.

• Process & Iteration: Detail your design process, including the evolution of your designs from initial concept to final product, highlighting significant iterations and the rationale behind them.

Process Documentation:

• Business Problem & Constraints: Clearly articulate the business problem being solved, along with any constraints (technical, time, budget) and assumptions made during the design process.

• User Needs Alignment: Demonstrate how your design decisions effectively met user needs while simultaneously furthering company objectives.

• AI Integration Rationale: Explain the specific role of AI in your design process and product outcomes, including how AI tools were leveraged and how AI behavior was shaped or managed.

📝 Enhancement Note: The portfolio requirements are heavily weighted towards demonstrating practical application of design skills within an AI-centric product development framework. Candidates should prepare detailed case studies that not only present the final design but also the journey, emphasizing the strategic use of AI, iterative improvements, and measurable business impact. Quantifiable results and clear articulation of the "why" behind design decisions, especially those influenced by AI, will be crucial.

💵 Compensation & Benefits

Salary Range: $210,000 - $250,000 annually.

Benefits:

• Equity (Stock Options)

• Generous health benefits

• Flexible time off policy

• Paid bonding time for all new parents

• Traditional and Roth 401k

• Commuter benefits

• FSA benefits

• Lunch Program

• Dog-friendly office

Working Hours: Standard full-time hours are expected, with the understanding that this is a 4-day on-site role in San Francisco. The total weekly hours are likely around 40, but the specific schedule may offer some flexibility outside of the mandated office days.

📝 Enhancement Note: The specified salary range is competitive for a Senior Product Designer role with specialized AI and design engineering skills in the San Francisco Bay Area. The inclusion of equity, variable pay, and comprehensive benefits indicates a robust total compensation package. The "4 day on-site" requirement is a key factor for work-life balance considerations.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology - AI, Data Analytics, Cloud Data Warehousing, Business Intelligence Applications. Sigma is at the forefront of transforming how businesses interact with and build applications on top of governed enterprise data.

Company Size: The provided data indicates "Sigma Computing" has over 1,000 employees (based on LinkedIn data). This suggests a mid-to-large-sized company with established processes but still retaining a dynamic, growth-oriented culture.

Founded: Sigma Computing was founded in 2017. This indicates a relatively young but established company that has achieved significant growth and funding rounds, suggesting a culture of innovation and rapid scaling.

Team Structure:

• The role reports into a Design team, but will work very closely with Engineering, Product Management, and Machine Learning Researchers.

• Given the "Senior" title and focus on AI-native UX, the designer will likely play a key role in shaping design strategy and potentially mentoring junior designers or design engineers.

Methodology:

• AI-Centric Development: The company emphasizes using AI to build applications, agents, and dashboards, indicating a core methodology of leveraging cutting-edge AI technologies.

• Data-Driven Product Development: Sigma's platform is built around enterprise data, suggesting that data analysis and insights are fundamental to their product strategy and design process.

• Agile & Iterative: The mention of "shipping faster," "iterating on code," and "prototyping in code" points towards an agile and iterative development methodology.

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

📝 Enhancement Note: Sigma Computing's recent Series D funding of $200M signals strong investor confidence and a commitment to aggressive growth and innovation, particularly in AI infrastructure and data application development. This context suggests a fast-paced, high-impact environment where new ideas are encouraged, and professionals are expected to contribute significantly. The company's focus on making data easily accessible for all users, combined with its advanced AI capabilities, creates a unique value proposition.

📈 Career & Growth Analysis

Operations Career Level: This role is classified as a Senior Product Designer, indicating a mid-to-senior level position. It requires significant independent contribution, strategic thinking, and the ability to influence product direction, especially within the specialized domain of AI-native UX.

Reporting Structure: While specific reporting lines are not detailed, a Senior Product Designer typically reports to a Design Lead, Director of Design, or Head of Product. The role involves extensive collaboration with engineering and product management teams.

Operations Impact: This role has a direct impact on Sigma's ability to deliver innovative AI-powered applications and analytics. By shaping AI interaction paradigms and ensuring the usability and trustworthiness of AI features, the designer significantly influences user adoption, product competitiveness, and ultimately, revenue generation through enhanced product capabilities. The role contributes to the company's mission of transforming BI through AI.

Growth Opportunities:

• Specialization in AI UX: Deepen expertise in the rapidly evolving field of AI-native user experience, becoming a leader in designing for intelligent systems.

• Technical Skill Development: Enhance coding and AI engineering skills through direct application and collaboration with ML experts, potentially leading to a Design Engineering or AI specialization.

• Leadership & Mentorship: Opportunities to set design direction, mentor junior team members, and influence the broader product strategy as a senior member of the design team.

• Product Impact: Contribute to groundbreaking product innovations in the AI and data analytics space, with the potential for significant career advancement within a high-growth tech company.

📝 Enhancement Note: The "Senior Product Designer, AI" title signifies a role that is both design-centric and technically adept. Growth here likely involves advancing within a specialized AI design track, potentially moving into leadership roles within design or even bridging into more technical AI product management or specialized AI engineering roles, given the hands-on coding and evaluation requirements.

🌐 Work Environment

Office Type: The role is hybrid, requiring 4 days on-site in the San Francisco office. This indicates a collaborative office environment designed to foster teamwork, brainstorming, and rapid iteration.

Office Location(s): San Francisco, CA. This location provides access to a vibrant tech ecosystem and talent pool.

Workspace Context:

• Collaborative Atmosphere: The 4-day on-site requirement suggests an emphasis on in-person collaboration, team synergy, and spontaneous idea exchange, crucial for complex design and AI development.

• Technology-Rich Environment: Expect access to modern design and development tools, including AI coding assistants, prototyping software, and potentially specialized hardware for AI development or testing.

• Cross-Functional Interaction: The workspace will facilitate regular interaction with engineers, product managers, and AI researchers, promoting a shared understanding and integrated approach to product development.

Work Schedule: While specific working hours are not detailed, a full-time schedule (likely 40 hours/week) is standard. The hybrid model allows for some flexibility on the remote day, but the on-site days are crucial for team integration and project momentum.

📝 Enhancement Note: The 4-day on-site requirement is a significant aspect of the work environment. It signals a company culture that values in-person collaboration for innovation and team cohesion, while still offering a degree of flexibility. The "dog-friendly office" and "Lunch Program" suggest a focus on employee well-being and a relaxed, yet productive, atmosphere.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Review of application materials, focusing on resume and portfolio alignment with AI and design engineering requirements.

• Portfolio Presentation: A dedicated session where candidates present their portfolio, highlighting specific AI-powered projects, design process, and impact. Expect to discuss how AI was used, the challenges faced, and the outcomes achieved.

• Technical/Design Challenge: Potential for a design exercise or a technical challenge related to AI interaction design, prototyping, or evaluating AI outputs. This may involve coding or designing solutions using AI tools.

• Team Interviews: Meetings with Product Managers, Engineers, and potentially ML Researchers to assess technical aptitude, collaborative skills, and cultural fit.

• Final Round: Discussions with leadership to evaluate strategic thinking, leadership potential, and overall alignment with Sigma's vision.

Portfolio Review Tips:

• Focus on AI Impact: Clearly articulate how AI was integral to the project's success, not just a supplemental tool. Quantify the impact where possible (e.g., "AI reduced design iteration time by X%", "AI-assisted feature led to Y% increase in user engagement").

• Showcase AI-Native Design: Present examples that demonstrate novel interaction paradigms for AI, trust signals, error handling, and explainability in AI-driven features.

• Detail Your Process: Explain your workflow, especially how you integrated AI tools (LLMs, coding assistants) into your design and development process. Show sketches, wireframes, prototypes (especially coded ones), and final released designs.

• Demonstrate Technical Skill: If you built prototypes in code or contributed directly to production code using AI assistance, highlight this capability clearly.

• Address Constraints & Business Goals: Clearly explain the business problem, user needs, and any constraints you worked within, and how your design solutions met these objectives.

Challenge Preparation:

• AI Interaction Patterns: Familiarize yourself with current best practices and emerging trends in conversational AI, LLM interfaces, and AI-driven workflows.

• Prototyping Tools: Be prepared to demonstrate proficiency with tools like Figma, and potentially tools like Cursor or Claude Code if a coding-based challenge is presented.

• Problem-Solving: Practice breaking down complex problems, especially those involving AI uncertainty, and articulating your approach to designing solutions.

• Communication: Prepare to articulate your thought process clearly and concisely, defending your design decisions and technical rationale to a mixed audience.

📝 Enhancement Note: The interview process is designed to rigorously assess a candidate's unique blend of design expertise and practical AI application. Candidates should be prepared to go deep on their AI-related projects, demonstrating not just conceptual understanding but also hands-on execution and a clear vision for the future of AI-native UX.

🛠 Tools & Technology Stack

Primary Tools:

• Figma: Industry-standard for UI/UX design, wireframing, and prototyping.

• Cursor: An AI-first code editor designed to integrate AI coding assistants directly into the development workflow.

• Claude Code (or similar LLM-based coding assistants): Tools for generating, debugging, and iterating on code through natural language prompts.

• LLMs (e.g., GPT-4, Claude): For clarifying scope, drafting specs, and generating creative ideas.

Analytics & Reporting:

• Sigma's own platform likely serves as a primary tool for data analysis and reporting, enabling designers to understand user behavior and product performance.

CRM & Automation:

• While not directly specified, standard CRM tools (like Salesforce) and marketing automation platforms might be part of the broader ecosystem the product interacts with.

• Integration tools are implied for connecting the Sigma platform with data warehouses and other enterprise systems.

📝 Enhancement Note: The explicit mention of Cursor and Claude Code, alongside LLMs, indicates a strong preference for candidates who are deeply integrated with AI-powered development workflows. Proficiency in these specific tools, or the ability to quickly adapt to them, will be a significant advantage. The role requires an understanding of how to leverage these tools effectively to augment design and engineering capabilities.

👥 Team Culture & Values

Operations Values:

• Innovation & AI Focus: A core value centered around pushing the boundaries of AI in data applications and analytics, encouraging experimentation with new technologies.

• Data-Driven Decision Making: Emphasizing the use of data and analytics to inform design choices, product strategy, and performance measurement.

• Collaboration & Cross-Functional Partnership: Fostering strong relationships between design, engineering, product, and research to build cohesive and effective solutions.

• User-Centricity: A commitment to understanding and meeting user needs, ensuring that complex data and AI capabilities are made accessible and intuitive.

• Efficiency & Speed: Valuing an agile approach that enables rapid iteration, quick deployment, and continuous improvement, often amplified by AI tools.

Collaboration Style:

• Integrated Teams: Expect a highly collaborative environment where designers work hand-in-hand with engineers and product managers on a daily basis.

• Open Communication: An emphasis on transparent communication, active listening, and constructive feedback exchange across all levels and functions.

• Shared Ownership: A culture where team members feel ownership over product outcomes, working together to achieve common goals.

• Fast-Paced Iteration: A dynamic environment that thrives on quick feedback loops and iterative development, where ideas can be rapidly prototyped and tested.

📝 Enhancement Note: Sigma Computing's culture appears to be a blend of cutting-edge AI innovation and practical, user-focused product development. The emphasis on "shipping faster" and leveraging AI suggests a culture that is both ambitious and execution-oriented. The "dog-friendly office" and "flexible time off" hint at a supportive and employee-focused workplace, balancing high performance with well-being.

⚡ Challenges & Growth Opportunities

Challenges:

• Defining AI-Native UX: Navigating the uncharted territory of designing user experiences for increasingly sophisticated AI systems, where traditional UX patterns may not apply. This includes managing user trust, AI explainability, and error handling in novel ways.

• Rapidly Evolving AI Landscape: Keeping pace with the exponential advancements in AI technology and integrating new capabilities and tools effectively into the product development lifecycle.

• Balancing AI Capabilities with User Needs: Ensuring that powerful AI functionalities are translated into genuinely useful and intuitive features for a broad range of users, not just technical experts.

• Technical Integration Complexity: Seamlessly integrating AI models, coding assistants, and evaluation frameworks into existing product architectures and workflows.

Learning & Development Opportunities:

• Cutting-Edge AI Research: Direct exposure to and contribution towards the application of advanced AI techniques in a commercial product setting.

• Mastering AI Design Tools: Gaining deep expertise in next-generation AI-powered design and development tools like Cursor and advanced LLMs.

• Specialized AI UX Training: Opportunities to attend conferences, workshops, or pursue certifications focused on AI ethics, conversational AI design, and human-AI interaction.

• Mentorship and Thought Leadership: Potential to become a recognized expert in AI-native UX, mentoring others and shaping the future of design at Sigma Computing.

📝 Enhancement Note: The primary challenge and growth opportunity lie in pioneering the field of AI-native UX. Candidates who thrive in ambiguity, are passionate about exploring new interaction paradigms, and possess a strong desire to learn and adapt will find this role exceptionally rewarding. The opportunity to shape the future of AI-driven products is significant.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used AI coding assistants to accelerate a product design or development cycle. What was the outcome, and what did you learn?" (Focus on tangible results, your role, and AI's specific contribution.)

• "How would you design an interface for a user to trust an AI's data analysis, especially when the AI's reasoning isn't fully transparent?" (Prepare to discuss trust signals, feedback mechanisms, and progressive disclosure.)

Company & Culture Questions:

• "Why Sigma Computing, and why this specific role at the intersection of design and AI engineering?" (Research Sigma's mission, funding, and product. Articulate your passion for AI in data.)

• "How do you approach collaboration with engineers and ML researchers, especially when dealing with the inherent uncertainties of AI development?" (Highlight your communication skills, empathy for different disciplines, and problem-solving approach.)

Portfolio Presentation Strategy:

• AI-First Case Study: Select 1-2 projects that best demonstrate your AI-native design capabilities. For each, clearly outline the problem, your role, the AI tools used, your design process (including iterations), the solution, and the measurable impact.

• Show, Don't Just Tell: Prepare interactive prototypes or live demos if possible. For coded prototypes, be ready to discuss the implementation details and how AI assisted.

• Quantify Impact: Wherever possible, use data and metrics to demonstrate the success of your designs. This could include user engagement, task completion rates, efficiency gains, or AI performance improvements.

• Articulate Technical & Design Rationale: Be prepared to explain the "why" behind your design decisions and technical choices, especially how they relate to AI capabilities and limitations.

📝 Enhancement Note: Candidates should prepare to speak fluently about both design principles and AI concepts. The interview will likely probe the depth of their experience with AI tooling and their ability to translate AI capabilities into user-centric product features. Demonstrating a proactive learning mindset and a passion for the future of AI in product development will be key.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided Greenhouse link.

• Portfolio Customization: Tailor your portfolio to prominently feature your most relevant AI-powered product work. Select case studies that clearly demonstrate your experience with AI coding assistants, conversational interfaces, and system-wide thinking in AI-driven products.

• Resume Optimization: Ensure your resume highlights keywords such as "AI Engineering," "Design Engineering," "LLM," "Conversational UI," "AI Coding Assistants," "Prototyping in Code," and relevant tools like "Cursor," "Claude Code," alongside your years of interaction design experience. Quantify achievements whenever possible.

• Interview Preparation: Practice presenting your portfolio, focusing on clearly articulating your process, the role of AI, and the impact of your designs. Be ready to discuss technical challenges and your problem-solving approach.

• Company Research: Deeply research Sigma Computing's product, mission, and recent funding news. Understand their position in the AI and data analytics market and how this role contributes to their strategic goals.

⚠️ 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.