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Who we are
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Video is 90% of the world's data. Most of it is invisible to machines.
TwelveLabs builds the intelligence layer to change that. Our multimodal AI models understand video the way humans do — across sight, sound, and motion — and power production\-scale AI workloads across media, entertainment, sports, security, and government.
We have raised more than $210 million from NEA, Radical Ventures, Amazon, NVIDIA, Snowflake, Databricks, Index Ventures, NAVER Ventures, Korea Investment Partners, Quadrille Capital, Red Bull Ventures, and AI pioneers including Fei\-Fei Li, Silvio Savarese, and Alexandr Wang.
We are a global company, headquartered in San Francisco with offices in Seoul, New York, and London, and employees around the world. We believe the differences in our cultural, educational, and life experiences make our products stronger. Building technology that understands the world in all its complexity requires people who see it from every angle. We are looking for individuals who are driven by hard problems and want their work to matter. Come build it with us!
About the Role
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We're hiring a senior AI engineer to own the integration layer that makes TwelveLabs' video AI accessible to the world.
Jockey is TwelveLabs' multimodal agent for video and image understanding. It's built on a knowledge store of ingested content, retrieval primitives like search, and an agent layer that orchestrates those primitives: planning, calling them, and reasoning over results to return grounded, cited answers. The engineer in this role owns the surfaces that make this system accessible to external developers and AI agents.
Learn more about Jockey here.
The engineer in this role decides how AI agents and developers connect to these capabilities, and you'll build the agent harness and the supporting infrastructure that makes that connection trustworthy at enterprise scale.
The center of gravity is our MCP server. You'll own it end\-to\-end: how agents discover and invoke our capabilities, what the tool interfaces look like, how failure modes are handled, and how the surface evolves as the agentic ecosystem does. Everything else, auth, metering, rate limiting, is the substrate that makes that surface something an enterprise customer will trust.
Location: San Francisco. Onsite or hybrid. No fully remote option.
In this role, you will own
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- *The agent\-facing integration surface.* TwelveLabs' MCP server and agent\-to\-agent interfaces are yours to design and operate. You'll decide how AI agents discover, connect to, and build on Jockey's retrieval and reasoning capabilities, and you'll be the person who knows what breaks and why.
- *The auth and access substrate.* OAuth, RBAC, and multi\-tenant isolation for enterprise customers. This is the work that turns a powerful capability into something a large organization will put in production. You'll design it from scratch rather than configure someone else's framework.
- *The enterprise\-readiness layer.* Per\-API\-key usage tracking, rate limiting grounded in real system constraints, and the reliability work that makes the platform trustworthy under real load. You'll define how the platform behaves under pressure before customers find out the hard way.
- *The research partnership.* You'll work directly with our research team to turn frontier video and image understanding capabilities into durable product surfaces. The loop from research to what customers can build is short, and you're a key part of closing it.
You may be a good fit if you have
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- You've shipped real agentic or LLM\-powered systems. Not demos, but production systems with real users and real failure modes you had to debug and fix. You can talk about what broke, why, and what you changed.
- You understand how retrieval, reasoning, and agent orchestration fit together as a system. Jockey's architecture sits at that intersection, and the integration surfaces you build need to reflect how that system actually behaves under real conditions.
- You've owned a developer\-facing or external API end\-to\-end, with real decisions about contracts, versioning, and the experience of building on top of your surface.
- You've built authentication and authorization in production, OAuth, OIDC, RBAC, or multi\-tenant isolation, as a primary owner and not a consumer of someone else's framework.
- You've thought seriously about enterprise\-readiness: what it takes to go from a capability that works to a platform that a large customer will trust with their data and workflows.
- You bring versatility across the full platform surface. We're not looking for a specialist in one of these areas. We're looking for someone who can own all of them and make good tradeoffs across them.
- You've operated in a fast\-moving environment before, a startup, a hypergrowth company, or an AI\-first team where you shipped under ambiguity without heavy process support.
Preferred Qualifications
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- Experience with MCP, A2A protocols, or the broader agent ecosystem. You've contributed to or built on these surfaces and understand where they're headed.
- Hypergrowth pedigree from a technically excellent startup.
Benefits and Perks
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An open and inclusive culture and work environment.
Work closely with a collaborative, mission\-driven team on cutting\-edge AI technology.
Full health, dental, and vision benefits
Extremely flexible PTO and parental leave policy. Office closed the week of Christmas and New Years.
VISA support where applicable
Compensation Range: $200K \- $240K
Salary Context
This $200K-$240K 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 Twelve Labs, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $200K to $240K.
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.
Twelve Labs AI Hiring
Twelve Labs has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $160K - $240K.
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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