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About This Role
Job Req: SLSQ427R270
Locations:
San Francisco/ Seattle
The Cloud \& AI Partnerships team is responsible for establishing durable long\-term alliances with the world's most important technology companies in the Data \& AI ecosystem.We are looking for a Sr. Alliance Director, AI GTM to facilitate Go\-To\-Market (GTM) strategies with our strategic AI partnerships — including our alliances with Anthropic, OpenAI, and Google Gemini. This role demands a rare combination of skills: the GTM fluency of a strategic alliance \& business development leader and the Generative AI domain expertise needed to build targeted programmatic GTM motions that create and convert pipeline across our global field organizations. This role requires established, existing relationships within the leadership/GTM functions of major AI labs and cloud providers, ensuring immediate credibility and accelerated collaboration.
You will set GTM strategy, manage co\-sell investment programs, build relationships between Databricks GTM executives and their counterparts at the world's leading AI labs, and drive measurable business outcomes through co\-sell and field enablement initiatives.
You will operate with a high degree of autonomy, report into the VP of Cloud \& AI Partnerships, and collaborate cross\-functionally with product \& engineering, sales, and marketing. This is a fast\-paced, high\-visibility role for someone who thrives in complexity and wants to be at the center of the most consequential partnerships in enterprise AI.
The impact you will have:
- Serve as the primary point\-of\-contact for senior GTM stakeholders at Anthropic, OpenAI, and Google, cultivating trusted, multi\-threaded relationships at each level of the org chart across sales and field engineering.
- Build and manage a QBR and Executive Business Review cadence with each AI partner, anchored on shared GTM goals and co\-sell programs.
- Develop and maintain partnership co\-sell KPIs and the overarching partner GTM governance framework to provide visibility into the health of programmatic co\-sell motions.
- Develop and execute an overarching co\-sell strategy across AI Lab partners, including account segmentation and propensity analysis to identify high\-impact joint opportunities.
- Plan and operationalize in\-person and virtual co\-sell programs — including co\-sell days, joint pipeline reviews, and partner\-led field events — to accelerate top\-of\-funnel growth and mid\-funnel conversion.
- Deliver regular program performance readouts to global sales and partnership leadership, surfacing insights and recommending course corrections.
- Build and manage streamlined partner funding programs designed to accelerate customer adoption of AI solutions
- Collaborate with enablement and commercialization teams within Databricks to drive board awareness \& utilization of funding and customer success programs.
- Partner with product, engineering, and commercial teams to shape the long\-term AI model integration roadmap and develop compelling 'better together' messaging for each AI Lab relationship.
- Collaborate with industry GTM teams, ISV and consulting partners, marketing, and corporate development to build joint sales plays, offers, and assets that grow top\-of\-funnel and increase conversion.
- Develop and maintain field enablement content to equip internal \& external field teams with the knowledge and tools to execute on joint GTM priorities and articulate differentiated 'better together' value propositions.
What we look for:
- 10\+ years of experience in business development, strategic partnerships, alliance management, or GTM strategy, with a meaningful portion of that time spent in the Cloud and AI ecosystem.
- Proven track record building GTM programs and growing high\-visibility partnerships with major technology companies, ideally cloud hyper\-scalers or frontier model providers.
- Deep understanding of Generative AI products, go\-to\-market models, and the competitive AI landscape, including familiarity with the positioning and capabilities of Anthropic, OpenAI, Google, and comparable frontier model providers.
- Experience in Generative AI product development, GTM, or alliance roles is strongly preferred
- Analytical rigor: able to build first\-principles business cases, model consumption, and derive data\-driven insights to support partnership investment and program prioritization decisions.
- Strong GTM fluency, including familiarity with enterprise co\-sell motions, account segmentation methodologies, pipeline management, and field enablement strategies.
- Excellent communication and executive presence — capable of distilling complex multi\-dimensional analyses into clear, compelling narratives for senior internal and external audiences.
- Strong cross\-functional leadership, ownership mindset, and bias for action. Comfortable driving deal and program processes autonomously while mobilizing large, matrixed organizations.
- MBA or equivalent advanced degree preferred. Bachelor's degree in Computer Science, Engineering, or related field, or demonstrated technical aptitude, is a plus.
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.
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 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
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.
Databricks AI Hiring
Databricks has 21 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span MD, US, Mountain View, CA, US, US. Compensation range: $225K - $360K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 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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