Staff Software Engineer, Applied AI (IC)

At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 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. Were 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, youll 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.
Build our agent platform and tooling
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.
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.
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 isnt — identify bottlenecks and failure modes across teams, not just within your own code.
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.

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.

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.

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 cant explain the logic, side effects, or trade-offs, it doesnt belong in the codebase.
We partner deeply with application teams to ensure AI actually changes how work gets done (and is trusted).
S. and are willing to consider remote candidates. #LI-Remote
recruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process.
This application period will remain open for 30 days. LI-REMOTE
In addition to your great compensation package, full-time employees will be eligible for the following perks:

~ Company-subsidized medical, dental, & vision plans
~Annual bonus
~ Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)
~ Generous paid leave programs, including 16-week paid parental leave and disability benefits
~ Company equipment provided (Windows & Mac options)
~ Annual performance reviews with opportunities for growth and career development

We are unable to sponsor or take over sponsorship of an employment Visa at this time.
All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.