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Senior Software Engineer, Applied AI (IC)

prizepicksRemotePosted 10 Sept 2026

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About this role

At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom.

Ready to reimagine the DFS industry together? Overview We're building an Applied AI engineering team focused on turning modern ML/LLMs into reliable, scalable product capabilities . This is a senior individual contributor role for someone who has operated on an Applied AI/Product AI team before — someone who can build, ship, and iterate quickly, while also raising the bar for engineering rigor and judgment in an agent-driven SDLC.

In the near term, you'll help us build our agent platform and internal tools , and partner with application engineering teams to operationalize AI across the software development lifecycle . Over time, the Applied AI team will increasingly shape product roadmap and strategy for AI-powered experiences — and increasingly, how the rest of engineering works day to day. What you'll do Build our agent platform and tooling Design and implement primitives like orchestration, tool/function calling, evaluation harnesses, prompt/version management, tracing/observability, and safety/guardrails.

Support patterns like retrieval-augmented generation (RAG), structured extraction, and production inference workflows. Operationalize AI in the SDLC Work with application engineering teams to embed AI into day-to-day engineering workflows (code review assist, test generation, incident support, developer copilots), with clear quality gates and measurable impact. Own the judgment call of what to delegate to agents vs. keep as human work — scope tasks so agents are set up to succeed, and adapt that boundary as team skill levels and tooling maturity vary.

Reason from the trace, not just the output Given an agent run, diagnose whether it did its job well from the trace itself — not just the final diff. Debug agent behavior the way you'd debug a distributed system: inputs, intermediate steps, failure points. Treat AI output as a claim to verify, not a result to trust — including tests the agent writes for its own code. Ship applied AI features end-to-end Own projects from prototype → production: data needs, system design, model/vendor selection, rollout plans, monitoring, and iteration.

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Partner closely with product, design, and data/ML stakeholders to deliver customer-facing outcomes (not just demos). Diagnose and evangelize AI adoption org-wide Assess where AI adoption is working and where it isn't — identify bottlenecks and failure modes across teams, not just within your own code. Build what's missing, teach what's already built, and make the case for adoption to skeptical or hesitant engineers. Mentor and set engineering standards Establish best practices for reliability, evaluation, incident response, privacy/security, and "how we build AI here." Coach other engineers through design reviews, pairing, and pragmatic technical leadership — including how to critically review AI-generated code rather than rubber-stamp it.

What we're looking for 6+ years of professional software engineering experience (or equivalent), including shipping production systems. Direct experience on an Applied AI / product AI team building LLM- or ML-powered features (agents/tooling strongly preferred). Demonstrated judgment in agent-driven development: knowing what to delegate, how to scope/context an agent, and how to verify its output before trusting it — this is as much a hiring bar here as raw coding ability.

Strong backend/system design skills: APIs, distributed systems, queues/workflows, observability, and performance. Comfort navigating ambiguity and driving outcomes with cross-functional partners (product, design, data/ML, platform). Track record of mentoring peers and leveling up engineering quality — including helping others build critical judgment around AI-assisted output, not just AI literacy. Nice to have Experience building "platform" capabilities for other engineers (SDKs, internal frameworks, developer tooling).

Practical experience with embeddings, search/retrieval, evaluation methodologies, and model monitoring (offline + online). Experience building AI-powered tooling/products like agents and assistants at scale. Familiarity with inference constraints (latency, cost, caching), vendor/model tradeoffs, and deployment patterns. Example scope / projects you might own Agent orchestration layer with tool routing, policy guardrails, and traceability.

RAG service with document ingestion, chunking/indexing, evaluation, and freshness controls. "AI in SDLC" rollout: automated PR review feedback + test plan suggestions, with measurement and safe rollout. Team-wide eval harness: goldens, regression tests, offline scoring + online experimentation. Org-level AI adoption diagnostic: where teams are stuck, what tooling gap explains it, and a plan to close it. How we work We bias toward shipping and iteration, with production-grade engineering standards.

We treat evaluation, observability, and safety as core product features — not afterthoughts. We hold the line that AI generates syntax, but the engineer remains the owner of every line committed — if you can't explain the logic, side effects, or trade-offs, it doesn't belong in the codebase. We partner deeply with application teams to ensure AI actually changes how work gets done (and is trusted). Where You’ll Live While we prefer candidates based in Atlanta, we are open to qualified applicants from anywhere in the U.S.

and are willing to consider remote candidates. #LI-Remote Working at PrizePicks: The typical salary range for this position is USD 220,000 to USD 250,000. At PrizePicks, we consider your role, level, and

Source listing: greenhouse_prizepicks

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