Staff AI Solutions Engineer
FanDuel is seeking a Staff AI Solutions Engineer to own the AI technical vision across its portfolio. This role involves evolving the technical strategy for the company's governed agent layer, strategy-optimisation engine, and simulation environment. The engineer will be responsible for making architectural decisions, producing design documentation, and acting as a primary technical voice for product teams.
FanDuel expects this individual to translate complex technical trade-offs into actionable decisions and provide evidence-based guidance to steer the team's direction. The role requires a balance of high-level strategic planning and hands-on technical leadership to ensure AI initiatives are effectively integrated into the engineering lifecycle.
- Drive the integration of the agent layer into the data engineering SDLC: requests arriving from Slack, Jira, GitHub, and PagerDuty, routed into a planning phase that produces scoped, reviewable tickets and a build phase that carries each ticket through spec, plan, build, test, PR, and documentation.
- Build the environment that makes agentic engineering actually work: skills, specs, context scaffolding, shared memory, and progressive disclosure.
- Extend governed, audited data access so agents operate under each user’s own permissions against our data platform, with a limited blast radius by design.
- Define the outer loop: continual learning, post-run review, and agent self-improvement that lands as reviewable pull requests rather than uncontrolled drift.
- Advance our strategy-optimisation engine from proof of concept to a capability product teams request by name, and land it against real business metrics.
- Partner with product to turn ambiguous business problems into optimizable ones: define the action space, the objective and utility weights that encode the tradeoffs we actually care about, and the guardrails and risk tolerance that keep the search honest.
- Own the deterministic scoring backbone: versioned scorers, shared and immutable experiment definitions, blind holdout discipline.
- Expand the engine to new scoring and optimization domains across the business, including generosity and promotional spend, module placement and ranking, and search relevance.
- Mature our offline simulation environment so product hypotheses can be pre-screened against synthetic personas built from real behavioral data, before committing scarce live A/B capacity.
- Act as the Team’s technical lead: take strategic direction and decompose it into specs, skills, and tickets that individual engineers can own end to end without waiting on you.
- Mentor engineers across the US and Romania pods, and write clearly, since not all collaboration is synchronous.
- Represent the team technically with partner engineering organizations, vendors, and external delivery partners on joint work.
- 8+ years of software, machine learning, or AI engineering experience, including technical leadership of a system other engineers built on (required)
- A track record of turning ambiguous business problems into measurable ones (required)
- Defining objectives, metrics, and guardrails alongside product partners rather than waiting to be handed a spec (required)
- Demonstrated breadth: credible in a technical design review, in a product prioritization conversation, and in front of senior stakeholders (required)
- Proven ability to decompose a vague strategic direction into workstreams that individual engineers can own autonomously (required)
- Expert-level Python in production settings: typing, testing, packaging, and performance awareness (required)
- Hands-on experience using agentic coding harnesses as real engineering tools, and designing the skills, context, tool access, and memory that make them effective (required)
- Production LLM application experience. Agent orchestration, tool and function calling, structured output handling, and retrieval (required)
- Strong optimization and experimentation background: search over strategy spaces, deterministic scoring, offline/online metric alignment, holdout discipline, and A/B design and interpretation (required)
- Cloud-native engineering on AWS, plus hands-on work in a modern data platform such as Databricks (required)
- Bachelor’s degree in Computer Science, a related technical field, or equivalent industry experience (required)
- Knowledge of sports and/or experience working within a heavily regulated industry (nice-to-have)
- Health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits
- Generous paid time off (PTO & sick leave)
- Annual bonus and long-term incentive opportunities (based on performance)
- 401k with up to a 5% match
- Commuter benefits
- Pet insurance
- 14 paid company holidays
FanDuel Group is an innovative sports-tech entertainment company that is changing the way consumers engage with their favorite sports, teams, and leagues. The premier gaming destination in the North America, FanDuel Group consists of a portfolio of leading brands across gaming, sports betting, daily fantasy sports, advance-deposit wagering, and TV/media, including FanDuel, Stardust Casino and TVG. The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia. FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).