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About This Role
SLSQ327R637
At Databricks, we are passionate about enabling GTM Analytics Engineering builds the internal data and AI platform that Databricks' own go\-to\-market organization runs on — used by 6,000\+ field users across sales, strategy, and operations. We own the number one Databricks App, the Genie agents, which are powered through the lakehouse (Unity Catalog bronze/silver/gold) beneath them. We are "Customer Zero" for Databricks: we adopt the newest Databricks and AI tooling first, in production, and pave the path for the rest of the company.
We're in the middle of a step\-change — moving the app from AI/BI and Genie experience to an AI\-native, genie\-centric platform that delivers *prescriptive* recommendations (not just descriptive reports), built to scale for the next 3–5 years. We build with AI in the loop end to end, and a large and growing share of our code is AI\-generated and human\-reviewed.
The role
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As an AI Engineer you'll help build the intelligence and application layers of that platform: Genie\-powered analytics agents, LLM\-driven workflows (feedback analysis, "what's new" enablement, BRD\-to\-plan automation), and the Databricks Apps and Lakebase services that serve them. You'll work alongside senior engineers, and you'll be expected to lean on AI coding tools heavily and thoughtfully from day one. This is a high\-ownership, fast\-shipping team where early\-career engineers get real surface area.
What you'll do
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- Build and ship features for AI\-powered surfaces: Genie analytics agents, LLM\-assisted feedback and enablement workflows, and in\-app intelligence inside the GTM Hub.
- Develop and integrate agentic workflows — prompts, tools/MCP integrations, retrieval, and evaluation — that turn business questions into reliable, grounded answers.
- Write Python and SQL against our Databricks lakehouse (Unity Catalog, Delta) and Lakebase (Postgres) services that back the apps.
- Use AI coding tools (Claude Code, agentic skills) as a core part of your workflow, and help build the reusable skills and harnesses that make the whole team faster.
- Contribute to evaluation and quality: help measure and improve the accuracy of our AI outputs (eval sets, scorecards, regression checks).
Partner with data engineers, app engineers, and strategy/ops stakeholders to take work from idea ticket PR* production.
- Write clean, well\-documented, tested code and participate in code review (human and AI\-assisted).
What we look for
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- Bachelor's degree in Computer Science, Data Science, or a related field — or equivalent practical experience.
- 7\+ years of software and AI engineering related experience.
- Solid fundamentals in Python and SQL.
- Hands\-on exposure to LLMs / generative AI — prior work building with APIs, prompts, RAG, Knowledge graphs \& agents.
- Genuine fluency with AI developer tools (e.g., Claude, Cursor) and a desire to push how far AI\-assisted engineering can go.
- Strong problem\-solving, curiosity, and a bias to ship and iterate; comfortable with ambiguity in a fast\-moving team.
- Clear written and verbal communication; works well with both engineers and non\-technical stakeholders.
Nice to have
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- Experience with the Databricks platform (AI Gateways, notebooks, jobs, Unity Catalog, Databricks Apps, Genie) or another cloud data platform.
- Building or evaluating agentic, RAG systems; familiarity with MCP, tool\-calling, or eval frameworks.
- Full\-stack exposure (React/TypeScript front end, FastAPI/Python back end) or data engineering (dbt, Spark, medallion architectures).
- Experience with Postgres/OLTP, CI/CD (Databricks Asset Bundles, GitHub Actions), or analytics/BI.
Our environment
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Databricks (AI Gateway, Unity Catalog, Delta, Jobs, Databricks Apps, Lakebase, Genie) · Python · SQL · React/TypeScript · FastAPI · Claude Code \& agentic skills · GitHub \+ DABs CI/CD. We work in sprints, review each other's PRs, and treat AI as a first\-class teammate — with the human judgment to know when it's wrong.
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non\-commissionable roles or on\-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job\-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.
Zone 1 Pay Range
$146,600—$201,650 USD
Zone 2 Pay Range
$132,000—$181,500 USD
Zone 3 Pay Range
$124,600—$171,400 USD
Zone 4 Pay Range
$117,300—$161,250 USD
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio\-economic status, veteran status, and other protected characteristics.
Compliance
If access to export\-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Salary Context
This $117K-$201K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Databricks, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($159K) sits 26% below the category median. Disclosed range: $117K to $201K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Databricks AI Hiring
Databricks has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span US, San Francisco, CA, US, New York, NY, US. Compensation range: $161K - $325K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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