Staff Software Engineer, Applied AI (IC)
Builds and scales an AI agent platform to integrate AI into the software development lifecycle, focusing on tooling, observability, and reliability for internal and product-facing AI features.
- Compensation
- $175K–$185K / yr
- Location
- Remote
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Full time
- Experience
- 6+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 6+ years of professional software engineering experience, 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
- 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
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
Full description
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