{"id":203838,"url":"https://alion.io/job/prizepicks-senior-software-engineer-applied-ai-ic","title":"Staff Software Engineer, Applied AI (IC)","company":{"id":98896,"name":"PrizePicks","domain":"prizepicks.com","url":"https://alion.io/company/prizepicks","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":86,"open_postings":9,"ghost_share":0,"stale_share":0.556,"repost_share":0,"time_to_fill_p50_days":35,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":220000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"RAG","optional":false},{"name":"Embeddings","optional":true},{"name":"Windows","optional":true}],"status":"live","first_seen_at":"2026-08-18T19:38:22Z","employer_posted_date":"2026-10-10","last_verified_at":"2026-10-18T16:08:23Z","board_verified":true,"closed_at":null,"days_open":60,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":60},"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? \nOverview\nWe'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.\nIn 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.\nWhat you'll do\n Build our agent platform and toolingDesign and implement primitives like orchestration, tool/function calling, evaluation harnesses, prompt/version management, tracing/observability, and safety/guardrails.\nSupport patterns like retrieval-augmented generation (RAG), structured extraction, and production inference workflows.\n\n Operationalize AI in the SDLCWork 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.\nOwn 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.\n\nReason from the trace, not just the outputGiven 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.\nTreat AI output as a claim to verify, not a result to trust - including tests the agent writes for its own code.\n\n Ship applied AI features end-to-endOwn projects from prototype → production: data needs, system design, model/vendor selection, rollout plans, monitoring, and iteration.\nPartner closely with product, design, and data/ML stakeholders to deliver customer-facing outcomes (not just demos).\n\nDiagnose and evangelize AI adoption org-wideAssess where AI adoption is working and where it isn't - identify bottlenecks and failure modes across teams, not just within your own code.\nBuild what's missing, teach what's already built, and make the case for adoption to skeptical or hesitant engineers.\n\n Mentor and set engineering standardsEstablish best practices for reliability, evaluation, incident response, privacy/security, and \"how we build AI here.\"\nCoach other engineers through design reviews, pairing, and pragmatic technical leadership - including how to critically review AI-generated code rather than rubber-stamp it.\n\nWhat we're looking for\n6+ years of professional software engineering experience (or equivalent), including shipping production systems.\nDirect experience on an Applied AI / product AI team building LLM- or ML-powered features (agents/tooling strongly preferred).\nDemonstrated 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.\nStrong backend/system design skills: APIs, distributed systems, queues/workflows, observability, and performance.\nComfort navigating ambiguity and driving outcomes with cross-functional partners (product, design, data/ML, platform).\nTrack record of mentoring peers and leveling up engineering quality - including helping others build critical judgment around AI-assisted output, not just AI literacy.\nNice to have\nExperience building \"platform\" capabilities for other engineers (SDKs, internal frameworks, developer tooling).\nPractical experience with embeddings, search/retrieval, evaluation methodologies, and model monitoring (offline + online).\nExperience building AI-powered tooling/products like agents and assistants at scale.\nFamiliarity with inference constraints (latency, cost, caching), vendor/model tradeoffs, and deployment patterns.\nExample scope / projects you might own\nAgent orchestration layer with tool routing, policy guardrails, and traceability.\nRAG service with document ingestion, chunking/indexing, evaluation, and freshness controls.\n\"AI in SDLC\" rollout: automated PR review feedback + test plan suggestions, with measurement and safe rollout.\nTeam-wide eval harness: goldens, regression tests, offline scoring + online experimentation.\nOrg-level AI adoption diagnostic: where teams are stuck, what tooling gap explains it, and a plan to close it.\nHow we work\nWe bias toward shipping and iteration, with production-grade engineering standards.\nWe treat evaluation, observability, and safety as core product features - not afterthoughts.\nWe 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.\nWe partner deeply with application teams to ensure AI actually changes how work gets done (and is trusted).\nWhere You’ll Live\nWhile 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\nWorking at PrizePicks:\nThe typical salary range for this position is $220,000 to $250,000. At PrizePicks, we consider your role, level, and where you'll be working when determining our salary ranges. The compensation info you see on our job postings gives you an idea of the starting pay range for the position. Your actual pay within that range will depend on your specific work location, as well as your skills, experience, and education. Your \nrecruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process. \n\nThis application period will remain open for 30 days. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting. \n\nDate Posted:9/10/26\nFirst Extension: 10/10/26\nSecond Extension:11/10/26\nBenefits you’ll receive:\nIn addition to your great compensation package, full-time employees will be eligible for the following perks: \nCompany-subsidized medical, dental, & vision plans \n401(k) plan with company match\nAnnual bonus\nFlexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)\nGenerous paid leave programs, including 16-week paid parental leave and disability benefits\nWorkplace flexibility and modern work schedules focused on getting the job done, not hours clocked\nCompany-wide in-person events and team outings\nLifestyle enhancement program\nCompany equipment provided (Windows & Mac options)\nAnnual performance reviews with opportunities for growth and career development\nYou must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. \nPrizePicks is an Equal Opportunity Employer. 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