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FanDuel

Runs sports betting and online casino apps

Staff AI Solutions Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeAtlanta, GA, USAIn-person

About the Job

Requirements

  • At least 8 years of software, machine learning, or AI engineering experience, including technical leadership of a system other engineers built on.
  • A track record of turning ambiguous business problems into measurable ones by defining objectives, metrics, and guardrails with product partners.
  • Breadth to contribute credibly in technical design reviews, product prioritization conversations, and discussions with senior stakeholders.
  • Ability to decompose a vague strategic direction into workstreams individual engineers can own autonomously.
  • Expert-level Python proficiency in production settings, including typing, testing, packaging, and performance awareness.
  • Hands-on experience using agentic coding harnesses as engineering tools and designing the skills, context, tool access, and memory that make them effective.
  • Production large language model application experience, including agent orchestration, tool and function calling, structured output handling, and retrieval.
  • Strong optimization and experimentation background, including search over strategy spaces, deterministic scoring, offline/online metric alignment, holdout discipline, and A/B design and interpretation.
  • Cloud-native engineering experience on AWS and hands-on work with a modern data platform such as Databricks.
  • Bachelor’s degree in Computer Science, a related technical field, or equivalent industry experience.

Responsibilities

  • Drive integration of the agent layer into the data engineering software development lifecycle, routing requests from Slack, Jira, GitHub, and PagerDuty into planning that produces scoped, reviewable tickets and a build phase that carries each ticket through specification, planning, building, testing, pull requests, and documentation.
  • Build the environment for agentic engineering, including skills, specifications, context scaffolding, shared memory, and progressive disclosure.
  • Extend governed, audited data access so agents use each user’s permissions against the data platform, with a deliberately limited blast radius.
  • Define the continual-learning outer loop, including post-run review and agent self-improvement delivered through reviewable pull requests.
  • Advance the strategy-optimization engine from proof of concept into a product capability, tied to real business metrics.
  • Partner with product to turn ambiguous business problems into optimizable problems by defining the action space, objective and utility weights, guardrails, and risk tolerance.
  • Own the deterministic scoring backbone through versioned scorers, shared immutable experiment definitions, and blind holdout discipline, ensuring results are comparable, auditable, and cumulative.
  • Expand the engine to additional scoring and optimization domains, including generosity and promotional spend, module placement and ranking, and search relevance.
  • Favor interpretable strategies that product partners can reason about, challenge, and implement without an external serving dependency.
  • Mature the offline simulation environment so product hypotheses can be pre-screened against synthetic personas built from real behavioral data before using live A/B capacity.
  • Establish directional agreement between simulated and live outcomes as the credibility bar, and communicate where simulation is and is not trustworthy.
  • Drive adoption of simulation as a standard input to experiment prioritization with product partners.
  • Act as the team’s technical lead by turning strategic direction into specifications, skills, and tickets that individual engineers can own end to end.
  • Set the working model for an AI-driven team, defining what humans specify, what agents build, and what humans review; raise the bar through code review, reusable patterns, and design review.
  • Mentor engineers across the United States and Romania pods and write clearly to support asynchronous collaboration.
  • Represent the team technically with partner engineering organizations, vendors, and external delivery partners on joint work.

Desired Qualifications

  • Knowledge of sports and/or experience working within a heavily regulated industry.

About the Company

FanDuel

Runs sports betting and online casino apps

FanDuel runs sports betting, online casino, and daily fantasy sports apps for adults in US states and Canadian provinces where gambling is legal. Two months after the Supreme Court let states legalize sports betting in 2018, the fantasy sports company opened its first sportsbook at New Jersey's Meadowlands Racetrack. Customers bet on games in the FanDuel Sportsbook app, play slots and table games on FanDuel Casino, wager on horse races through FanDuel Racing, and trade yes-or-no event contracts on FanDuel Predicts. FanDuel keeps the difference between what players stake and what they win. Founded in 2009 and based in New York City, it is owned by Flutter Entertainment, the parent of Paddy Power and PokerStars.

Company size
1,001-5,000
Company stage
Private
Total funding
$435.9M
Headquarters
New York City, New York
Founded
2009

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