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.
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 $270,000 to $290,000. 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
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. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting.
Date Posted:9/10/26
First Extension: 10/10/26
Second Extension:11/10/26
#LI-REMOTE
Benefits you’ll receive
In addition to your great compensation package, full-time employees will be eligible for the following perks:
Company-subsidized medical, dental, & vision plans
401(k) plan with company match
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
Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked
Company-wide in-person events and team outings
Lifestyle enhancement program
Company equipment provided (Windows & Mac options)
Annual performance reviews with opportunities for growth and career development
You 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.
PrizePicks is an Equal Opportunity Employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
