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About This Role
Who We Are
DV is the leader in digital performance solutions, helping our advertiser and agency partners Verify the quality of their digital campaigns, Optimise to improve performance and Prove that they’re achieving their business outcomes, through unbiased 3rd party data and analytics. DV’s mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world’s largest brands, agencies, publishers, and digital ad platforms. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry. Learn more at www.doubleverify.com.
About the Role
Over the past year, DV built an AI platform at speed. More than 800 people now use Claude and Cursor daily against an MCP gateway that fronts 30+ internal tool backends, a gateway for model traffic, a registry where engineers publish skills and plugins, and a tracing pipeline running off every laptop. It was built by a strong, opinionated group of engineers who continue to drive it forward, and it is already in wide production use.
This is the first role at DV dedicated entirely to that platform. You'll join the engineers building it today with AI infrastructure as your full focus, which means having the time to go deep on the hardest problems and keep the platform moving day to day.
A lot of it is already live and needs operating and hardening. Plenty is still ahead of us: group-based authorization across the gateway, a consolidated multi-region model gateway with real failover, and cost attribution that teams can act on.
What You Will Do
- Run and harden the MCP gateway day to day. It runs on Envoy Gateway on GKE, secured by Keycloak, serving 30+ backends to Claude Code, Cursor and Claude Desktop across the company, plus an externally reachable gateway. You'll be one of the first responders when it breaks.
- Take the lead on closing the authorization gap: group-based access from JWT claims, per-tool RBAC, and scoped OAuth consent in place of the static user lists and broad scopes we run on today.
- Help consolidate the model gateway estate onto one supported deployment and drive it to GA, with multi-region, health-checked failover and coverage beyond a single client.
- Push AI observability to GA with the team. Finish the trace pipeline, extend it across tools, and give teams per-session, per-user and per-department cost and latency numbers they can trust.
- Build out the infrastructure behind AI cost governance: spend caps that reflect real dollars rather than raw tokens, plus attribution, enforcement and alerting.
- Support the registry, eval and marketplace platform that lets engineers publish and consume skills, plugins and MCP servers safely, and partner with Security on model and data access control, prompt injection exposure and connector supply chain review.
- Work across a DevOps org spread over three regions, pairing with the engineers who built each piece.
Who You Are
Required Qualifications
- 5+ years in DevOps, Platform Engineering or SRE, with production Kubernetes ownership and the software engineering habits to go with it.
- Hands-on experience managing and scaling AI infrastructure in production: Demonstrated track record operating developer AI tools, model routing/gateway platforms, or LLM observability stacks at organizational scale.
- Real depth in OAuth 2.0/2.1, OIDC and JWT: token exchange, scope design, claim propagation, and federation between an IdP, an authorization server and a proxy. Some of the hardest problems on this platform live here.
- Production experience with a gateway, proxy or service mesh (Envoy or equivalent), and the judgment to debug a misbehaving control plane under pressure.
- A track record of taking infrastructure from prototype to GA. You've set the SLOs, built the failover, written the runbooks and carried the pager.
- The collaboration skills to work well inside an opinionated, distributed engineering group. You build on other people's work, disagree constructively, and write things down.
Preferred Qualifications
- Upstream contributions to Envoy Gateway or a comparable open-source proxy.
- LLM observability tooling (Langfuse, LangSmith, Arize Phoenix) and OTEL GenAI conventions.
- Cost attribution or chargeback for a shared platform.
- Keycloak or equivalent IdP engineering.
- Zero-trust access platforms and secrets management.
Our Stack
GCP/GKE, Envoy Gateway, Keycloak, Okta, Terraform, ArgoCD, Helm, Vault, Kafka, Redis, Prometheus/Grafana/Alloy, Langfuse, LangSmith, GitLab CI, Python and Go.
This position is full-time and located in our New York City headquarters offices offering a hybrid work model from office and remote.
The successful candidate’s starting salary will be determined by a number of non-discriminatory factors, including qualifications for the role, level, skills, experience, location, and internal equity relative to peers at DV.
The estimated salary range for this role, based on the qualifications set forth in the job description, is between $153,000 $260,000. This role will also be eligible for bonus/commission (as applicable), equity, and benefits.
The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles and recognize that the person we hire may be more or less experienced than this job description as posted.
Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you’re not sure that you check every box, apply anyway!
Salary Context
This $153K-$260K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At DoubleVerify, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $153K to $260K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
DoubleVerify AI Hiring
DoubleVerify has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Remote, US, New York, NY, US. Compensation range: $196K - $260K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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