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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
This role centers on managing a portfolio of clients and partnering with sales leaders in leading tech and consulting firms to accelerate business growth through innovative technology solutions. Key objectives include onboarding new customers, ensuring their success and retention, and proactively expanding relationships. Responsibilities range from seamless customer onboarding and enablement, tailored AI solution delivery, and workflow optimization to driving customer satisfaction, adoption, and renewal rates. The role also involves conducting business reviews, providing dedicated support, monitoring account health and consumption, and cultivating strong executive relationships—all with the aim of maximizing customer value, fostering long\-term partnerships, and sustaining high net retention.
Key Responsibilities
- Lead Value\-Driven Pre\-Sales Engagements: Deliver compelling, outcome\-focused demonstrations aligned to customer priorities. Lead technical discovery to assess workflow fit and data feasibility, and identify opportunities to drive expansion through increased platform adoption
- Customer Retention and Growth: Focus on retaining existing customers and expanding accounts by delivering tailored business solutions that enhance sales pipelines and account intelligence.
- Own Seamless Post\-Sales Delivery: Partner closely with customers and Applied AI to ensure a smooth transition from pre\-sales to delivery. Work with Agent Builders to deploy agentic workflows, and lead go\-live readiness including QA and UAT to ensure successful customer outcomes.
- Onboarding and Enablement: Ensure seamless onboarding and enablement processes for new customers, aligning with their business objectives.
- Shape The Future of AI GTM Product Innovation: Collaborate with Customers and our Product and Engineering teams to identify new agentic patterns that drive GTM productivity.
- CSAT and Adoption (Renewals): Drive high customer satisfaction and adoption rates to secure renewals and long\-term partnerships.
- Develop and execute a multi\-year strategic account plan: Including stakeholder maps, expansion roadmaps, and mutual success plans aligned to Salesforce's internal priorities
- Orchestrate co\-sell and co\-marketing initiatives with Salesforce's ISV partnership and AppExchange teams to generate joint pipeline
- Demand Generation (Workflow Expansion): Identify opportunities to expand workflows and drive demand generation efforts.
- Guiding Workflow Choices: Assist customers in selecting optimal workflows to maximize go\-to\-market productivity.
- Building a Champion Network: Develop strong relationships with key stakeholders to build a network of champions within client organizations.
- Quarterly Business Reviews: Conduct regular business reviews to assess progress and align strategies with customer goals.
- Point of contact Account Management to ensure customer success and satisfaction.
- Key Metrics include: Net Retention (Focus on maintaining a high net retention rate), Account Consumption (Monitor and optimize account consumption of deployed workflows), Account Relationship Health (Foster strong relationships with workflow owners to ensure healthy account dynamics)
What you’ll bring
- 7\+ years in strategy and operations roles, with at least 3 years at a top\-tier strategy firm (such as McKinsey, Bain, BCG) or within a Big 4 strategy practice, leading go\-to\-market (GTM) and commercial excellence programs.
- C\-Suite and Boardroom Presence: Regular engagement with C\-level executives, providing strategic counsel and building trusted relationships at the highest organizational levels.
- Deep RevOps and Technology Expertise: In\-depth understanding of the revenue operations technology stack—including CRM, CMS, ABM, and Sales Intelligence platforms—as well as hands\-on experience applying AI/ML solutions to optimize revenue operations and customer success strategies.
- Familiarity with Sales, Salesforce and the Sales SaaS/AI tech domain
- Strategic and Operational Agility: High emotional intelligence with a reputation as a change agent who can balance big\-picture strategic framing with hands\-on, sleeves\-rolled\-up execution.
- Academic Credentials: Bachelor’s degree required; MBA or advanced degree preferred.
*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: $150K \- $180K
Salary Context
This $150K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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: $180K - $180K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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