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About This Role
MKTQ227R16
Databricks is building a new generation of native applications built on the Databricks Platform that help customers unlock value from their data faster. Lakewatch is the first of these apps, giving cybersecurity, governance, and risk teams an open, agentic Security Lakehouse that unifies security, IT, and business data into a single, governed environment for AI\-powered detection, investigation, and response.
We are looking for a Sr. Product Marketing Manager to support the marketing strategy for these emerging applications to support Lakewatch in its next phase of growth. In this role, you will shape and execute the go\-to\-market strategy and tactics for Lakewatch and other applications in the Agentic Apps portfolio, collaborating closely with demand generation, sales, partners, external agencies and contractors, and the broader Databricks marketing organization to drive awareness, adoption, and expansion for this first\-of\-its\-kind motion at Databricks.
This role sits within the product marketing organization and works cross\-functionally with demand generation, product, sales, and alliances.
The impact you will have:
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- Hit or exceed Lakewatch and Agentic Apps pipeline and revenue goals by shaping and executing the GTM strategy that drives new logo acquisition and expansion within existing Databricks customers, with clear targets for sourced and influenced pipeline, ARR, and product adoption over the first 12–24 months.
- Execute content and field marketing activities across Agentic Apps by building and deploying scalable marketing programs that increase awareness, improve perception, and drive demand for Databricks Agentic Apps, with an emphasis on decision makers and senior executives in the line of business domain areas.
- Increase Lakewatch and Agentic Apps adoption and usage across priority customer segments by co\-creating a compelling narrative and positioning that differentiates Databricks in its various application spaces with an emphasis on cybersecurity, measurably improving win rates, deal sizes, and attach rates for security use cases.
- Establish Databricks as a recognized leader in its App Domains, with an emphasis on Cybersecurity \- by driving thought leadership, field activity, content creation, sales collateral, analyst and influencer engagement, and presence at key industry events, measured through share of voice, coverage, and pipeline contribution and progression
- Deliver high\-impact product and feature launches that translate roadmap investments into business results, meeting agreed\-upon targets for adoption, activation, and usage of new capabilities within defined timeframes after launch.
- Improve sales and overlay effectiveness in sales opportunities by building and scaling sales plays, messaging, and enablement that increase Lakewatch and Agentic Apps win rates and reduce sales cycle times across core regions and segments.
- Grow partner\-sourced and partner\-influenced pipeline by building joint narratives, solutions, and programs with cloud and technology partners, tied to specific pipeline and revenue goals.
- Continuously optimize GTM performance by defining the right metrics, instrumenting programs, and using data and customer feedback to iteratively improve conversion across the funnel (awareness, evaluation, adoption, and expansion).
What we look for:
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- Extensive product marketing experience in enterprise security software, with meaningful exposure to security operations, SIEM/security analytics, or adjacent domains where you have successfully driven pipeline and revenue for security offerings.
- Proven ability to build and scale GTM for new or early\-stage products, including setting goals, defining content and field marketing strategies, and running campaigns and launches that demonstrably improved pipeline, ARR, or adoption.
- Experience in both hands\-on execution and 3rd party agencies for content and events in running demand generation, awareness, and field marketing activity
- Experience in “outbound” product marketing such as content marketing, field marketing, working with owned and 3rd party events teams, external agencies and partners, to drive results and maintain a high level of excellence in execution
- Attention to detail and operational thinking that uses sound judgment to determine the right levels of execution for cross\-product marketing initiatives – from practitioner activity like 3rd party conference sponsorships and demand gen content creation, to executive level events like Executive Forums.
- Demonstrated success partnering with product and demand generation to bring market and customer insight into roadmap decisions, with clear examples where your input led to capabilities that increased usage, customer value, or retention.
- Experience enabling global sales and partner organizations on new solutions or categories, with evidence that your plays, content, field activity, and enablement improved product line outcomes such as win rates, pipeline progression, sales cycle times, or attach rates.
- Experience collaborating with technology partners or ecosystems to build joint narratives and programs that generated partner\-sourced or partner\-influenced pipeline.
- Strong communication, comfortable driving cross\-functional initiatives, influencing without direct authority, and representing Databricks with customers, partners, analysts, and at industry events.
- Comfort operating in a fast\-paced, highly cross\-functional environment, where you help define new motions and processes rather than simply optimizing established playbooks.
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
$163,400—$224,750 USD
Zone 2 Pay Range
$147,100—$202,300 USD
Zone 3 Pay Range
$139,000—$191,050 USD
Zone 4 Pay Range
$130,700—$179,750 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 $130K-$224K 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 17% below the category median. Disclosed range: $130K to $224K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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