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About This Role
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At Databricks, we are passionate about enabling data teams to solve the world's most challenging problems — from making the next mode of transportation a reality to accelerating medical breakthroughs. We do this by building and running the world's best data and AI platform, enabling our customers to use deep data insights to improve their businesses. Our ability to execute this mission depends on building trust and recognition within an ever\-growing community of data engineers, analysts, scientists, machine learning practitioners, and AI practitioners.
Are you the person who is already at every AI meetup in the Bay Area — not to be seen, but because that's where the interesting conversations are happening? Are you a hands\-on practitioner in generative AI, agents, and MLOps who can hold your own in a room full of AI engineers and leave them with something they can actually use?
As a Senior Developer Advocate for AI and Machine Learning, you'll be the voice of Databricks in the Bay Area AI community and a crucial link between our engineering teams and the global community of data scientists and AI engineers. You'll focus on how practitioners build, evaluate, govern, and ship agentic systems on the Databricks Data \+ AI Platform — with particular depth in Genie Agents, Genie Code, Genie One, MLflow, and Databricks Unity AI Gateway. You'll translate what you learn from customers and the community into reference implementations, demos, and content that are production\-ready rather than merely impressive on stage.
This is a role for someone who wants to be in the room. San Francisco and the wider Bay Area are the densest concentration of AI practitioners in the world, and we want Databricks to be present and useful in that community — at meetups, hack nights, demo nights, founder dinners, and the hallway conversations around them. You'll build genuine relationships with the people building the future of AI here, and you'll bring what you hear back to our product and engineering teams.
Your responsibilities will encompass a wide range of knowledge\-sharing activities. You'll deliver engaging talks, host panels and meetups, create blogs and video content, develop courseware, answer questions in community forums, and meet one\-on\-one with influential AI engineers and data scientists.
Reporting to the Head of Developer Relations, you'll collaborate with fellow Developer Advocates and program managers to create and execute a cohesive and impactful developer relations strategy.
The ideal candidate embodies the values of our Developer Relations team: a deep passion for data and AI, genuine empathy for developers' needs, and a strong commitment to explaining our products clearly and honestly.
### More about the DevRel team
The Developer Relations (DevRel) team at Databricks is dedicated to building and fostering relationships with communities of data practitioners. Our goal is to drive awareness and adoption of the Databricks Data \+ AI Platform — including Lakeflow, Lakebase, Genie Agents, and AI/BI — alongside the open source projects that underpin it, such as Apache Spark™, Delta Lake, Unity Catalog, MLflow, Apache Iceberg™, and Omnigent. Our team also owns programs like the Databricks MVP program, the user group program, and DevConnect roadshows.
### The impact you will have
- Be a consistent, recognized presence in the Bay Area AI community — speaking at local user groups and meetups, showing up to the events that matter, and building real relationships with the practitioners and founders shaping the space.
- Own medium\-to\-large advocacy projects end\-to\-end, from scoping through delivery, for the AI and agents area.
- Create production\-ready reference implementations, starter kits, and demos that show practitioners how to build, evaluate, and govern agents on Databricks — and get them to first value in minutes, not hours.
- Create high\-quality educational content — videos, sample notebooks, datasets, tutorials, courseware, and blog posts — and reuse each core asset across formats to reach practitioners wherever they already are.
- Drive awareness and adoption of Databricks AI capabilities through speaking engagements at industry events and conferences.
- Act as a trusted advisor between the AI community and our product teams, and influence roadmap discussions with what you learn.
- Expand and nurture the Databricks AI and ML community by growing meetups and user groups and supporting practitioners in online communities.
- Gather and analyze community feedback to drive continuous improvement of Databricks products and services.
- Mentor and unblock less\-experienced advocates in content, demos, and public speaking.
### What we look for
- 5\+ years of combined experience as a developer advocate and in a hands\-on technical role — software engineer, ML engineer, data scientist, or solutions architect.
- Professional experience actually building and operating AI or ML systems, not only presenting about them.
- Subject matter knowledge of solving AI and ML problems at scale, including agent design, evaluation, and production operations.
- Comprehensive understanding of the Databricks AI ecosystem, including Genie Agents, Genie, MLflow, and Databricks Unity AI Gateway.
- Strong Python skills and fluency with the current agent and ML tooling landscape.
- Based in the Bay Area and genuinely willing to be out in the community on a regular basis, including evenings.
- Active participation and recognized leadership in AI and ML community forums, chats, and meetups — measured by what the community builds with your help, not by follower count.
- A public portfolio of technical work: tutorials, talks, repositories, or sustained community contributions.
- Proven track record in nurturing developer communities, organizing user groups, and facilitating meetups.
- Exceptional communication skills, with a talent for articulating complex concepts through writing, teaching, video, and public speaking.
- Deep empathy for developer needs, with the ability to craft engaging experiences across tutorials, notebooks, and community platforms.
- Adept at collaborating with cross\-functional stakeholders to align community initiatives with product objectives.
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 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 anticipates 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
$149,200—$205,150 USD
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT\&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data \+ AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30\+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
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.
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Salary Context
This $149K-$205K range is above 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($177K) sits 18% below the category median. Disclosed range: $149K to $205K.
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 San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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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