Staff AI Solutions Engineer
FanDuel · Atlanta, Georgia, United States
About this role
**Staff AI Solutions Engineer — FanDuel (Hybrid)** ## The Position FanDuel is hiring a **Staff AI Solutions Engineer** to help own the **AI technical vision** across the portfolio. You’ll evolve and govern the technical direction across: - the **governed agent layer** - the **strategy-optimization engine** - the **simulation environment** In this role, you’ll make architectural calls, write clear **design docs** and **decision records**, and serve as a trusted technical voice for other teams. You’ll translate tradeoffs into actionable decisions—and push back with evidence when a direction is wrong. ## The Game Plan ### Drive AI Into the Engineering Lifecycle - Integrate the **agent layer** into the data engineering SDLC (Slack/Jira/GitHub/PagerDuty → planning → scoped tickets → spec/plan/build/test/PR/docs). - Build the environment that makes **agentic engineering** work (skills, specs, context scaffolding, shared memory, progressive disclosure). - Extend **governed, audited data access** so agents operate under each user’s permissions with limited blast radius. - Define the **outer loop**: continual learning, post-run review, and agent self-improvement delivered as reviewable PRs (not uncontrolled drift). ### Make Optimization a Product Capability - Move the **strategy-optimization engine** from proof of concept to a capability teams request by name, grounded in real business metrics. - Partner with product to turn ambiguous problems into optimizable ones (action space, objective/utility weights, guardrails, risk tolerance). - Own deterministic scoring (versioned scorers, shared/immutable experiment definitions, blind holdout discipline) so results are comparable, auditable, and cumulative. - Expand into new optimization domains (generosity/promotional spend, module placement/ranking, search relevance). - Bias toward **interpretable strategies** product partners can reason about and implement without external serving dependencies. ### Prove Ideas Before They Ship - Mature the **offline simulation environment** using synthetic personas from real behavioral data to pre-screen hypotheses before live A/B capacity. - Establish credibility between simulated and live outcomes—and be transparent about where simulation is/isn’t trustworthy. - Drive adoption of simulation as a standard input to experiment prioritization. ### Turn Vision Into Work the Team Can Own - Act as the team’s technical lead: decompose strategy into specs/skills/tickets engineers can own end-to-end. - Set the working model for an AI-driven team (human specifies → agent builds → human reviews), raising the bar via code review, reusable patterns, and design review. - Mentor engineers across **US and Romania pods**, writing clearly for async collaboration. - Represent the team with partner engineering orgs, vendors, and external delivery partners. ## The Stats (What We’re Looking For) - **8+ years** software, ML, or AI engineering experience, including technical leadership of systems other engineers built. - Proven ability to turn ambiguous business problems into measurable ones (objectives, metrics, guardrails with product partners). - Breadth across technical design reviews, product prioritization, and senior stakeholder communication. - Ability to decompose vague strategy into autonomous workstreams. - **Expert-level Python** (typing, testing, packaging, performance awareness). - Hands-on experience with **agentic coding harnesses** and designing skills/context/tool
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