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About This Role
Join the Future of Commerce with Whatnot!
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Whatnot is the largest livestream shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
As a remote co\-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
We're one of the fastest growing marketplaces and were recently named the \#1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.
The Role
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We're looking for a Senior AI Engineer to build the internal tools, prototypes, and business workflows that put AI into the hands of every team at Whatnot—and to set the patterns the rest of the company builds on. You'll own ambiguous, cross\-org bets end\-to\-end: finding real problems, shipping working software fast, hardening what works, and scaling how Whatnot gets value out of AI.
This is a high\-velocity, high\-judgment builder role. You've shipped a lot of apps and prototypes—you're someone who can sit with a CX lead or an ops manager, understand their workflow in an afternoon, and have a working prototype in their hands by the end of the week. But you also operate above any single project: you decide where the leverage is, define the reusable patterns and infrastructure others adopt, and raise the bar for how the whole org builds with AI. You move fluidly across the stack (frontend, backend, data, and the AI layer) and are deeply fluent in the current generation of AI tools and the production patterns around them—prompt engineering, RAG, MCP, agents, and evals.
This is not a model\-training or research role. You won't be training models—you'll be building the tools, workflows, and integrations that make off\-the\-shelf AI dependable and useful inside Whatnot's systems and processes.
What You'll Do
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- Own ambiguous, cross\-org AI bets end\-to\-end—identify the highest\-leverage problems across the company, decide what to build, and drive it from prototype to durable production tool
- Build and ship a high volume of internal apps, prototypes, and automations—going from a vague problem to a working tool in days, then iterating with users toward production quality
- Define the reusable patterns and shared infrastructure the org builds on—reference architectures, internal libraries, MCP servers, eval harnesses, and templates that let others move faster and safer
- Embed directly with teams across CX, Trust \& Safety, ops, GTM, and EPD to find high\-leverage problems, then build the solution alongside them
- Wire AI into real business context—building RAG and retrieval pipelines, MCP servers, and agentic workflows grounded in Whatnot's data, with appropriate PII and access controls
- Integrate AI tools with internal systems and data sources via APIs, connectors, and event\-driven workflows so automations act on real state, not toy inputs
- Scale the leverage—package successful builds into reusable skills and playbooks, and level up the whole company through enablement sessions, boot camps, and mentorship of other builders
- Stay ahead of the AI landscape, evaluating and bringing in new models, tools, and patterns as the ecosystem evolves, and making the build\-vs\-buy calls
Who You Are
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- A prolific builder with senior judgment—you ship fast and iterate with real users, and you know where *not* to invest; you measure yourself by problems solved, tools adopted, and leverage created
- Deep applied\-AI fluency—extensive hands\-on experience building with the current generation of LLM products (Anthropic, OpenAI, Google) and the production patterns around them: prompt engineering, RAG, MCP, agents, and evals
- Systems\-integration chops—you've connected AI to real data and tools through APIs, webhooks, and connectors, and you reason rigorously about reliability, latency, cost, and access controls
- A pattern\-setter and force multiplier—you've defined how teams build, mentored other engineers, and raised the technical bar for an org, not just your own output
- A strong communicator—you can sit with non\-technical users, translate their problems into software, and teach them to build for themselves
- Low ego, high agency—you see inefficiency, build the fix, and bring people along with you
Benefits
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- Flexible Time off Policy and Company\-wide Holidays (including a spring and winter break)
- Health Insurance options including Medical, Dental, Vision
- Work From Home Support
+ Home office setup allowance
+ Monthly allowance for cell phone and internet
- Care benefits
+ Monthly allowance for wellness
+ Annual allowance towards Childcare
+ Lifetime benefit for family planning, such as adoption or fertility expenses
- Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
- Monthly allowance to dogfood the app
+ All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!).
- Parental Leave
+ 16 weeks of paid parental leave \+ one month gradual return to work \*company leave allowances run concurrently with country leave requirements which take precedence.
EOE
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Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.
Compensation Range: $200K \- $260K
Salary Context
This $200K-$260K 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 Whatnot, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($230K) sits 27% above the category median. Disclosed range: $200K to $260K.
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.
Whatnot AI Hiring
Whatnot has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $215K - $260K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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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