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About This Role
Voice AI that resolves, not transfers.
Most phone systems trap callers in menus and scripts. Vapi is the platform for deploying voice agents that know your business and can listen, adapt, and resolve in minutes.
- The numbers: 1 billion calls. 1 million developers. 10x enterprise ARR growth
- The customers: Amazon Ring, ServiceTitan, New York Life, Intuit, Kavak, and thousands more, from YC startups to the Fortune 500
- The news: a $50M Series B led by Peak XV Partners, with Bessemer Venture Partners, Kleiner Perkins, M12 (Microsoft's Venture Fund), Y Combinator, and our earlier backers. Total raised: $72M
Product Designer — AI UX / Product Experience
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Location: SF (hybrid Tuesday\-Thursday)
About Vapi (/ˈVɑːpi/):
We're creating the shift to voice as humanity's default interface.
We're the most configurable platform for deploying voice agents.
We've grown to over 1M developers, adding thousands every day.
Try talking to Vapi now!
Why We're Hiring This Role
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We're building the next generation of AI product experiences and need someone who shapes product direction, not just executes on it. We want a designer who can help us see problems in new ways, raise the quality of our thinking, and create clear, high\-leverage product experiences in a fast\-moving AI environment.
What You'll Do
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30 Day:
Go deep on our complex, technical workflows. Understand the product surface end to end and how customers actually use it.
60 Day:
Own an ambiguous problem area outright. Partner closely with engineering and product to turn it into a clear direction, and ship high\-quality work quickly and iteratively.
90 Day:
Contribute novel ideas and patterns that help define how AI\-native workflows should work in practice. Be a voice that raises the level of the team, not just execution capacity.
Who You Are
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- 5\+ years experience: Exceptional candidates with less considered case by case
- Exceptional taste: You bring product and design judgment, not just solid execution; strong fundamentals and a modern design sensibility
- AI\-Forward: You design with and around AI\-augmented workflows, not just for AI products
- A rigorous thinker: You frame ambiguous problems clearly and ask sharp questions before jumping to solutions
- A systems thinker: With range, not at the expense of creativity and product insight
- B2B / enterprise background: You've designed for enterprise or B2B products, not legacy enterprise software (think Linear or Vercel, not Salesforce, Workday, or Microsoft)
- Comfortable with complexity: You're at home designing highly technical products and workflows
- Fluent in AI: You have up\-to\-date intuition for AI products, tooling, and workflow design
- Provably original: Your work has introduced new ideas, patterns, or ways of thinking to a team
- Fast in fast\-moving environments: You've designed and shipped inside iterative, high\-velocity product orgs
Strong Candidates May Also Have:
- Experience at companies known for distinctive product craft or novel UX patterns (Linear, Vercel, and similar next\-gen AI product companies)
- Experience designing AI\-native, workflow\-heavy, or developer\-facing products
- A track record of shipping in ambiguous, high\-ownership environments
- A portfolio that shows both strong thinking and real execution
- Technical fluency, whether from a CS background, building your own tools, or years on developer\-facing products
Not A Fit If:
- Primarily a mobile or consumer designer with no pull toward complex B2B
- Bootcamp\-only design background with no broader technical grounding
- No prior tech industry experience
How We'll Evaluate This Role
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- How you think through ambiguous problems
- Whether you bring genuinely new perspectives
- Whether you can translate insight into shipped product quality
- Whether your work raises the bar for the team
Why Vapi
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- Generational impact: Build the human interface for every business
- Ownership culture: 70% of the company are previous founders
- Kind team: The founders, Jordan and Nikhil, are Canadians
- Tier\-1 Investors: Peak XV, Kleiner Perkins, Bessemer, M12, YC
What We Offer
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- Real stake: We offer a competitive salary and excellent equity ownership
- Comprehensive health coverage: medical, dental, and vision plans
- Team love: We love hanging out, and we do quarterly off\-sites
- Flexible time off: Take what you need
- More: Catered meals, transportation, gym, and a $10k annual L\&D budget
Compensation Range: $180K \- $280K
Salary Context
This $180K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At vapi, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($230K) sits 5% above the category median. Disclosed range: $180K to $280K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
vapi AI Hiring
vapi has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $280K - $280K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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