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About This Role
Job Description:
DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future.
As a Lead AI Delivery Manager on the DataRobot Federal Professional Services team, you are the primary owner of client engagement, technical delivery, and long\-term relationship health across a portfolio of federal government customers. You are a trusted advisor who bridges the gap between what our customers need and what DataRobot can deliver — guiding agencies through complex AI adoption journeys with the credibility of a seasoned consultant and the accountability of a program manager.
You will own the full customer lifecycle for your assigned accounts: from early engagement and solution scoping, through delivery execution and stakeholder alignment, to expansion and renewal. You will be the face of DataRobot to the customer — building executive relationships, managing delivery risk, and ensuring that commitments made in the sales cycle are met with rigor and professionalism in the field.
While you will bring sufficient technical depth to credibly engage with government IT teams, architects, and data teams, your primary value is not as a hands\-on engineer. It is as a strategic delivery leader who can orchestrate the right internal and partner resources, navigate federal procurement and compliance requirements, and translate complex AI capabilities into clear, mission\-relevant value for senior government stakeholders.
*Please note: You must be a U.S. Citizen with an active U.S. DoD TS/SCI security clearance to be eligible for this role. CI Poly is strongly preferred. If you do not meet these criteria, you will not be considered for the position.*
What Does It Take?:
The right candidate brings a blend of consulting instincts, delivery discipline, and relationship\-building skills, backed by sufficient technical fluency to navigate federal AI/ML environments credibly. Specifically:
- Own the customer relationship end\-to\-end — serving as the primary point of contact and trusted advisor for executive, program, and technical stakeholders across your account portfolio.
- Lead technical project delivery with rigor — develop and manage detailed project plans, track milestones, surface and mitigate risks, and hold both internal teams and customer counterparts accountable to agreed commitments.
- Translate customer mission needs into DataRobot solutions — conducting discovery, identifying use cases with the highest ROI, and structuring engagements that deliver measurable outcomes.
- Drive account growth and retention — identify expansion opportunities, support renewal conversations, and partner with Sales to build a compelling business case for continued and expanded investment in DataRobot.
- Lead compliance with security requirements including ATO processes and IL5, IL6 and TS authorization timelines.
- Navigate federal procurement including agency\-specific acquisition vehicles (e.g., JWCC, Tradewinds, GSA Schedule, OTA).
- Provide consultative architecture guidance — engaging federal IT and data teams on integration patterns, platform deployment approaches, and data pipeline design, while knowing when to bring in deeper technical specialists.
- Serve as the voice of the customer internally — synthesizing field learnings and customer feedback to inform Product, Engineering, and Sales priorities for the Federal market.
- Support pre\-sales activities — participating in customer discovery, scoping professional services engagements, contributing to proposals, and presenting at executive briefings.
- Mentor and coordinate partner delivery resources — overseeing subcontractors and SI partners engaged on customer programs, ensuring quality and consistency of delivery.
Is This You?:
Consulting \& Advisory Excellence:
- Client Engagement Leadership: 7\+ years of professional services, management consulting, or technical advisory experience in federal or defense markets. You have a proven track record leading complex, multi\-stakeholder engagements from discovery through delivery, and have served as the primary relationship owner for government program offices at the GS\-15 / SES / O\-6 level and above.
- Solution Architecture \& Scoping: Demonstrated ability to translate ambiguous agency requirements into concrete, scoped engagements with clear deliverables, timelines, and success criteria. You are comfortable leading whiteboard sessions, facilitating working groups, and producing professional SOWs and technical proposals.
- Executive Communication: Outstanding written and oral communication skills. You can command a room of senior government officials, distill complex AI concepts for non\-technical audiences, and produce polished executive briefings, status reports, and program reviews.
Technical Project Management:
- Delivery Ownership: PMP, PMI\-ACP, CSM, ICAgile, or equivalent experience managing technology delivery programs in federal environments. You build and maintain detailed project plans, run structured status cadences, and proactively surface risks before they become escalations.
- Multi\-Workstream Coordination: Experience managing concurrent delivery tracks involving internal DataRobot teams, government IT staff, systems integrators, and subcontractors. You understand how to allocate resources, sequence dependencies, and keep programs on schedule when priorities shift.
- Risk \& Issue Management: You maintain a living risk register, distinguish between issues that need immediate escalation and those that can be managed in\-stream, and communicate status to leadership with clarity and appropriate urgency.
- Federal Program Familiarity: Comfortable operating within federal program management frameworks including Agile/SAFe and CDRL deliverable structures. Experience with contract vehicles such as JWCC, GSA Schedule, and OTA is a plus.
Customer Relationship Management:
- Account Health Ownership: You monitor leading indicators of account health (usage, satisfaction, engagement breadth) and intervene proactively when risk signals emerge. You know the difference between a customer who is satisfied and one who is at risk, and you act on that distinction long before renewal conversations begin.
- Expansion \& Retention Mindset: You consistently identify new use cases, stakeholders, and funding streams that could deepen DataRobot’s footprint within existing accounts. You proactively surface opportunities to introduce agentic AI solutions — orchestrating multi\-step, mission\-aligned workflows that move customers beyond standalone predictions and toward measurable operational impact. You partner closely with your Sales counterpart to convert delivery success into commercial momentum.
- Stakeholder Mapping \& Navigation: You maintain a detailed understanding of the decision\-making landscape within your accounts — who the champions are, who the skeptics are, and how to build coalitions across program offices, budget holders, and CIO organizations.
Technical Fluency (Federal AI/ML Context):
- AI/ML Methodology, Lifecycle, and Platform Knowledge: Working familiarity with AI/ML methodologies, lifecycle, and platforms, MLOps concepts, and the infrastructure considerations for deploying AI in federal environments. You don’t need to write the code, but you need to credibly engage with the team that does.
- Python Knowledge: Working knowledge of Python sufficient to engage in meaningful reviews of developer code
- Federal Security \& Compliance: Working knowledge of the ATO lifecycle, Risk Management Framework (RMF), FedRAMP authorization processes, and \\IL5/IL6 deployment requirements for classified networks. You can speak to these frameworks fluently in customer conversations and know when to bring in deeper security expertise.
- Cloud \& Integration Awareness: Sufficient familiarity with cloud platforms (AWS GovCloud, Azure Government) and common federal data architectures to guide scoping conversations and identify integration complexity early.
- Agentic AI Delivery: Demonstrated experience scoping and delivering agentic AI solutions in production — including multi\-step agent workflows, tool\-calling integrations, and human\-in\-the\-loop oversight — ideally for federal or other regulated customers.
Education, Clearance \& Logistics:
- Education: Bachelor’s in Computer Science, Engineering, Business, or a related field; advanced degree or MBA a plus.
- Clearance: Active TS/SCI clearance required; CI Poly strongly preferred.
- Citizenship: Must be a U.S. citizen.
- Travel: Willingness to be on\-site at client facilities (DC metro area) up to 90% during active delivery phases, with approximately 15% regional travel for executive engagements, program reviews, and conferences.
Bonus Points:
- PMP, PMI\-ACP, CSM, or SAFe certification.
- Prior experience as a management or technical consultant at a firm with a federal practice (e.g., Booz Allen, Deloitte Federal, Accenture Federal, SAIC, Leidos).
- Demonstrated experience growing federal accounts year\-over\-year through proactive land\-and\-expand strategies.
- Familiarity with DataRobot or comparable enterprise AI/ML/data platforms (H2O, SAS Viya, Dataiku, Palantir, Databricks).
- Experience working within or alongside NGA program offices.
- AWS Certified Solutions Architect or similar cloud certification.
Citizenship and Clearance Requirements:
- Must be a U.S. citizen.
- Must have an active TS/SCI clearance.
- CI Poly clearance preferred.
The talent and dedication of our employees are at the core of DataRobot’s journey to be an iconic company. We strive to attract and retain the best talent by providing competitive pay and benefits with our employees’ well\-being at the core. Here’s what your benefits package may include depending on your location and local legal requirements: Medical, Dental \& Vision Insurance, Flexible Time Off Program, Paid Holidays, Paid Parental Leave, Global Employee Assistance Program (EAP) and more!
DataRobot Operating Principles:
- Wow Our Customers
- Set High Standards
- Be Better Than Yesterday
- Be Rigorous
- Assume Positive Intent
- Have the Tough Conversations
- Be Better Together
- Debate, Decide, Commit
- Deliver Results
- Overcommunicate
All DataRobot hires are required to complete a background check prior to starting employment, which includes identity verification, criminal history check, employment verification and education verification. Additionally, all DataRobot employees must be available to attend in\-person company trainings and meetings.
Research shows that many women only apply to jobs when they meet 100% of the qualifications while many men apply to jobs when they meet 60%. At DataRobot we encourage ALL candidates, especially women, people of color, LGBTQ\+ identifying people, differently abled, and other people from marginalized groups to apply to our jobs, even if you do not check every box. We’d love to have a conversation with you and see if you might be a great fit.
DataRobot is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. DataRobot is committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. Please see the United States Department of Labor’s EEO poster and EEO poster supplement for additional information.
All applicant data submitted is handled in accordance with our Applicant Privacy Policy.
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 DataRobot, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
DataRobot AI Hiring
DataRobot has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, Remote, US.
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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