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About This Role
About Databook
Databook is the AI guided selling platform and decision-support system built for enterprise GTM teams. Its three-layer architecture—verified customer-back intelligence, structured agentic reasoning, and guided coaching with closed-loop analytics—gives revenue organizations the consistency, confidence, and leadership visibility they need to win complex enterprise deals. Databook's customers drive more than $500B in annual revenue and include Salesforce, Microsoft, Databricks, Konica Minolta, and Schneider Electric. Learn more at www.databook.com.
Role summary: Build the revenue operating system for enterprise selling
For twenty years, enterprise sales has run on Systems of Record: CRMs that capture what happened, but do little to help a seller decide what to do next. Databook is building the revenue operating system that replaces them. It starts from a shared World Model of the companies, people, strategies and events in the market, so every account arrives already understood. On top of it, Databook reasons over the account, the deal and the seller's context, links events over time, and recommends the next best action with the evidence behind it.
We are looking for a Director of AI Product Development to lead how that product is designed, built, tested and shipped for some of the largest sales organizations in the world. You will report to the CEO, spend real time with customers, work hand in hand with our Applied AI engineering, Data & Insights and GTM AI Strategy & Deployment teams, and lead a team of Agent Builders who configure our agentic workflows and build quality assurance into them. Customer deployments are run by our AI Strategy & Deployment team; your focus is the product they deploy.
This is not a technical product management role. We are looking for sharp judgement, a deep understanding of how enterprise selling actually works, and refined product taste: someone who can tell the difference between an AI output that is technically correct and one a CRO or seller will act on. You have shipped AI applications into enterprise production, you know the domain, and you want a leadership role where the learning curve is steep and the impact is direct, working with some of the most sophisticated go-to-market teams in the world.
What you'll do
Product (about 40% of your time)
- Define the product experience. Turn our product strategy into agentic workflows that sellers and sales leaders rely on every day. Bring clear, opinionated judgement on what we build, what we do not build and what good looks like, and use adoption and business impact to decide what to build next.
- Build on the World Model. Design how sellers work in our CRM layer: opportunities that start on companies already in the World Model rather than on empty records, actions and outcomes captured as they happen, and pipeline and forecasting that run alongside a customer's existing CRM.
- Partner with Data & Insights. Work closely with the team that defines and maintains what Databook knows about every kind of company. Make sure the coverage, freshness and confidence of that data show up in the product where sellers need them, and that the gaps you see in customer questions shape what the team collects next.
Delivery & Quality (about 30%)
- Design, build and ship AI applications. Partner with Applied AI engineering to take agentic workflows and next best action recommendations from concept and prototype through evaluation, testing and production release, and keep improving them once they are live.
- Lead and grow the Agent Builder team. Lead and develop a team of Agent Builders, growing in 2027, who configure our agentic workflows and build quality assurance into them. Set the standard for how that work gets done.
- Build quality into every workflow. Define what quality means for AI output a seller will act on, and make it measurable through evaluation sets, acceptance criteria, regression testing and production monitoring.
Customers (about 30%)
- Stay close to customers. Spend time with sellers, sales leaders, marketing, customer success, enablement and RevOps teams at our enterprise customers. Understand where deals are won and lost, and bring that reality back into the product.
- Partner with GTM. Work with our AI Strategy & Deployment team, who run customer deployments, so that what we sell, build and deliver stay aligned. Turn patterns from the field into reusable product capabilities rather than one-off customization.
- Educate customers. Help design and deliver our AI Bootcamps (roughly one week per quarter), teaching customer sales and enablement teams how to work with AI and get the most from Databook.
What you'll bring
- 6 to 10 years of experience building and shipping software for enterprise customers, including product leadership responsibility and experience leading or developing people. You are ready for a broader leadership role, take high ownership and learn fast.
- Strong enterprise sales domain knowledge. You have built products used daily by enterprise GTM teams, and you understand account planning, complex deal cycles, forecasting and the reality of CRM data. You care about changing how selling works, not just automating today's process.
- A proven record of designing, building, testing and deploying AI applications (LLMs, agents, retrieval, tool use) in enterprise production, with real users depending on them. You understand evaluations, context design and the failure modes of LLM output, and you can prototype ideas yourself with AI tools, even though your edge is judgement rather than code.
- Refined product taste. You notice when an output is generic, when a workflow adds friction, or when an answer would not hold up in front of a CRO, and you fix it.
- Confidence with senior customer stakeholders, including CROs and other executives, with clear, concise communication and the ability to run a session that ends in a clear decision.
Nice to have
- Experience building on or for sales AI or GTM platforms, or in the Salesforce ecosystem.
- Experience with knowledge graphs, entity resolution or other data products where the quality of the underlying data shapes the user experience.
- Experience designing or delivering customer training, enablement or bootcamp programs.
- Prior experience at a top-tier consulting firm (McKinsey, Bain, BCG), advising CxOs and influencing large scale transformations.
- Experience working with distributed teams across the US and globally.
First 90 Days - What success looks like
- Customer immersion. You have spent time with a range of our enterprise customers, observed their sellers at work, and can explain where Databook creates the most value and where it falls short.
- A product point of view. You have developed and tested new product concepts with the CEO and customers, and agreed priorities with Applied AI, Data & Insights and GTM.
- A quality bar in place. Every new workflow ships against an agreed evaluation and QA standard, and you can report on output quality across live customers.
- A team with a clear operating model. You have taken ownership of the Agent Builder team with a defined way of building, testing and handing off workflows, and you have taught in your first AI Bootcamp.
Databook provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, pregnancy, military and veteran status, age, physical and mental disability, genetic characteristics, or any other considerations made unlawful by applicable state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Databook expressly prohibits any form of workplace harassment based on race, color, religion, sex, national origin, pregnancy, military and veteran status, age, physical and mental disability, or genetic characteristics.
Compensation Range: $160K - $240K
Salary Context
This $160K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Databook Labs, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($200K) sits 6% below the category median. Disclosed range: $160K to $240K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Databook Labs AI Hiring
Databook Labs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US. Compensation range: $240K - $240K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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