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About This Role
At JetBrains, we build intelligent tools for developers and development teams. More than 15 million developers and over 300,000 companies worldwide rely on our products to solve real, complex problems. Our mission is simple: make development teams more productive and bring more joy to software development.
Inside the Professional Services department, we are building a team of Forward Deployed Engineers (FDEs) to enable JetBrains consulting partners to transform their customers' software development lifecycles and to help our customers adopt AI\-native development with JetBrains Central, our new platform for agent\-driven software development.
JetBrains Central connects tools, agents, and infrastructure so enterprises can adopt automated AI workflows with full visibility into results, costs, and processing speed. It provides three core capabilities:
- Governance and control: Policy enforcement, identity and access management, observability, auditability, and cost attribution for agent\-driven work, with some of these capabilities already available via the JetBrains Console.
- Agent execution infrastructure: Cloud agent runtimes and computation provisioning that allow agents to run reliably across development environments.
- Agent optimization and context: Shared semantic context across repositories and projects, enabling agents to access relevant knowledge, as well as task routing to the most appropriate models or tools.
### About the role
JetBrains has been building developer tools since 2000, and today, 420 of the Fortune 500 rely on our products. As a Lead FDE, you will work directly with these customers to deploy JetBrains Central into complex enterprise environments and help early adopters move from experimental AI usage to governed, scalable AI\-native development.
You will act as a trusted advisor to customer engineering teams and consulting partners, mentor other FDEs, and be responsible for our most complex deployments. Your work in the field will directly influence how our platform evolves and how enterprises adopt agent\-driven development.
This role combines consulting, architecture, and hands\-on engineering. It is often a strong fit for engineers who have previously served as Principal or Staff Engineers, Heads of Engineering, CTOs, or Solutions Architects – and who want to stay close to both customers and complex technical systems.
### What you'll do:
- Architect and manage deployments of JetBrains Central into enterprise development environments, CI/CD systems, and internal developer platforms.
- Mentor and support other FDEs, sharing architectural expertise and scaling the team's deployment practices.
- Drive organizations' transition from ad\-hoc AI experimentation to governed, AI\-native development workflows.
- Help organizations establish their own AI governance, including security policies, access management, and data privacy controls.
- Enable observability for AI\-driven development, helping organizations understand their AI usage and code\-generation patterns, as well as AI’s impact on productivity.
- Design sustainable AI adoption strategies, including for usage controls and cost visibility across multiple model providers.
- Integrate our cloud\-based agent execution layer so AI agents run securely and at scale.
- Partner closely with JetBrains product teams, shaping the platform based on real\-world deployments and customer feedback.
- Create delivery frameworks and train JetBrains partners to deliver the desired scope of service.
### You will thrive in this role if you are:
- A consultant at heart who communicates effectively with both engineers and technical leadership.
- Able to see the whole picture of the software development lifecycle across developer tools, infrastructure, APIs, and cloud systems.
- Experienced at integrating complex software systems into enterprise environments.
- Comfortable working with early adopters and overcoming ambiguous technical challenges.
- Motivated to mentor other engineers and help build a new technical discipline.
### What you will bring:
- 10\+ years building or operating production software systems, with at least 3 years in a customer\-facing technical role (Solutions Architect, Forward Deployed Engineer, Principal Consultant, or similar).
- Proficiency in Java or Python, with the ability to read, debug, and extend customer code in production environments.
- Experience building or integrating developer platforms, infrastructure systems, or software solutions.
- Demonstrated experience navigating enterprise procurement, security review, and architecture review processes.
- Familiarity with modern development workflows (CI/CD, developer environments, internal developer platforms, etc.).
- Strong problem\-solving skills and the ability to operate in fast\-evolving technical environments.
- Willingness to travel domestically to customer sites (see *Location and travel* below).
### These would be a plus, but are not blockers:
- Hands\-on experience with LLM evaluation, prompt engineering at scale, or agent orchestration frameworks (LangGraph, AutoGen, CrewAI, or similar).
- Experience operating in environments regulated by SOC 2, FedRAMP, HIPAA, or similar.
- Experience with industry\-specific solutions (fintech, telco, retail, healthcare, etc.).
- Prior experience in solutions engineering, consulting, or forward\-deployed engineering roles.
- Familiarity with the JetBrains ecosystem or developer productivity tooling.
### Location and travel
- This role is remote, based in the US.
- This role involves up to 20% travel to customer sites, primarily within North America.
### Compensation \& Benefit
Benefits include medical / dental / vision insurance, FSA and 401k plans, generous PTO, parental leave, learning \& development opportunities.
This role is also eligible for an annual bonus based on the company policy.
\#LI\-KP1
*This range reflects the employer’s good\-faith estimate of the base salary it reasonably expects to pay for the position at the time of posting. Starting pay within the range will be determined based on job\-related factors such as skills, qualifications, experience, and work location.*
The base salary range (per year) for this position in the posted work location(s) is
$204,000 \- $306,000 USD
We are an equal opportunity employer
We know great ideas can come from anyone, anywhere. That’s why we do our best to create an open and inclusive workplace – one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation.
*We process the data provided in your job application in accordance with the* *Recruitment Privacy Policy.*
Salary Context
This $204K-$306K 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 JetBrains, 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 ($255K) sits 41% above the category median. Disclosed range: $204K to $306K.
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.
JetBrains AI Hiring
JetBrains has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $306K - $306K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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