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About This Role
Sigma is transforming how businesses allow customers to build apps, agents and dashboards on top of governed enterprise data. Hence, we are growing the design team and looking for designers who are excited to solve challenging problems, deliver impactful capabilities throughout our stack to build world\-class technology. You will be part of a talented team of designers with a shared mission to make data easily accessible for all users.
We're looking for a Senior Product Designer / Design Engineer who sits at the intersection of interaction design and AI engineering: someone who uses AI to ship faster, builds the skills and evals that make AI more effective, and invents new interaction paradigms for how people work alongside intelligent systems.
This isn't a traditional design role. Yes you'll be using Figma, but also writing code with AI, training it, evaluating it, and questioning every assumption about what a "UI" can be when the interface itself reasons.
Please note this is a 4 day on\-site role in our San Francisco office.
What You'll Do
- Start with AI, stay with AI.Use LLMs to clarify scope, draft specs, surface edge cases, and align your team before committing to a direction, use AI coding tools to build and iterate on the solution itself, and merge code to prod when fits.
- Prototype in code. Build working interfaces with Cursor and Claude Code, guiding structure, behavior, interaction, motion and UX quality while AI handles implementation. Partner directly with engineering to decide what moves into the product and what stays as a validated spike.
- Bring it to production. Fix small interaction and refinement issues directly on prod code.
- Design new AI interaction paradigms for conversational interfaces. Invent and validate novel patterns for how users converse with, direct, and trust AI systems \- especially in data contexts where precision and confidence matter.
- Write evals, skills, and help on tools. Build the scaffolding that makes AI reliable: evaluation frameworks, reusable skills, and tooling that helps Sigma's AI features behave predictably and improve over time.
- Shape model behavior. Iterate on evals and training to improve AI performance on design output. In other words: teach AI how to be a great designer.
- Set the craft bar. Define what great AI\-native UX looks like at Sigma: patterns and principles the broader team builds on.
What We're Looking For
- 4\+ years in interaction design or design engineering, with recent hands\-on AI tooling experience — not just prompting, but building.
- Demonstrated use of AI coding assistants to ship real product (and side projects). You can describe what you built, how AI accelerated it, and where you had to steer it.
- Experience writing evals, agents, prompt libraries, or skills for AI systems. You understand why evaluation is the hardest part of AI product development.
- Strong interaction design fundamentals: you know when to reach for a new pattern and when to extend a familiar one, and you can defend that choice to anyone.
- Opinions about AI UX: trust signals, error recovery, progressive disclosure of reasoning and real examples of how you've addressed these.
- Exceptional ability to communicate design decisions to PMs, engineers, ML researchers, and executives.
- A portfolio that includes AI\-powered or AI\-adjacent product work.
Portfolio Requirements
- Transformed intricate interaction problems into elegant flows: bonus if includes conversational interfaces
- We want to see designs that have been successfully released that demonstrate system\-wide thinking, breadth and depth in interactive design skills and AI first
- We want to see your process and progressive evolution of your designs, including the business problem you were trying to solve, constraints and assumptions you needed to work within, where the design started and your significant iterations leading to the final product.
- We want to understand how your designs furthered company objectives while meeting user needs
The base salary range for this position is $210,000 \- $250,000 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work at Sigma Computing. This role is eligible for a variable pay (based on goal achievement), stock options, as well as a comprehensive benefits package.
About us:
Sigma is the AI Apps and agentic analytics platform built on the cloud data warehouse. Business and technical teams use Sigma to explore live data, build intelligent applications, and automate critical workflows all without moving data or breaking governance. Sigma supports a spreadsheet interface, SQL, Python, and native AI in a single governed workspace, giving every team the speed to act and IT the control to scale. Sigma is trusted by more than 2,000 customers, including AMD, Duolingo, Colgate\-Palmolive, and JPMorgan Chase.
Sigma announced its $80M in Series E financing in May 2026\. The round was led by Princeville Capital, with new strategic investors Databricks Ventures, ServiceNow Ventures, and Workday Ventures participating alongside returning investors Altimeter Capital, Avenir Growth Capital, D1 Capital Partners, K5 Global, NewView Capital, Spark Capital, Sutter Hill Ventures, and XN. This milestone follows Sigma reaching $200M in annual recurring revenue in April 2026, with more than 100% year\-over\-year growth and 1\.1 million new active users added in the latest fiscal year.
Come join us!
#### Benefits For Our Full\-Time Employees:
- Equity
- Generous health benefits
- Flexible time off policy. Take the time off you need!
- Paid bonding time for all new parents
- Traditional and Roth 401k
- Commuter and FSA benefits
- Lunch Program
- Dog friendly office
Sigma is an equal opportunity employer. We are committed to building a smart and strong team regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, veteran, or any other protected status. We look forward to learning how your experience can enable all of us to grow.
*Note:* *We have an in\-office work environment in all our offices in SF, NYC, London and Sydney.*
Our Privacy Practices
When you submit a job application on this site, Sigma processes your personal data for the purposes of evaluating your candidacy for employment at Sigma and as otherwise needed throughout the recruitment and hiring process. Please review Sigma's Candidate Privacy Notice for more details. Please note that your personal data may be transferred to a country other than the one in which it was provided (including to the USA, the UK, and Canada, Australia).
Sigma's use of AI
This hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision\-making.
Salary Context
This $210K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Sigma Computing, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($230K) sits 27% above the category median. Disclosed range: $210K to $250K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Sigma Computing AI Hiring
Sigma Computing has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $250K - $270K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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