Senior Software Engineer, Applied AI (IC)
New
P
PrizePicksSports Technology
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.Full-TimeSenior
Salary$175,000 to $185,000
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Job Details
- Experience
- 6+ years
- Required Skills
- Backend DevelopmentMachine LearningSoftware EngineeringLLMDistributed Systems
Requirements
- 6+ years of professional software engineering experience, including shipping production systems.
- Direct experience on an Applied AI or product AI team building LLM- or ML-powered features.
- Proven experience with agent-driven development and tooling.
- Strong backend and system design skills (APIs, distributed systems, queues/workflows, observability).
- Ability to navigate ambiguity and drive outcomes with cross-functional partners.
- Demonstrated judgment in scoping AI tasks and verifying model output.
- Track record of mentoring peers and establishing high-quality engineering standards.
- Experience with embeddings, search/retrieval, and evaluation methodologies is a plus.
- Experience building platform capabilities like SDKs or internal frameworks is a plus.
- Familiarity with inference constraints (latency, cost, caching) is a plus.
Responsibilities
- Build and maintain agent platform tooling, including orchestration, tool/function calling, evaluation harnesses, and observability.
- Support production AI patterns like retrieval-augmented generation (RAG), structured extraction, and inference workflows.
- Partner with application engineering teams to integrate AI into development workflows like code reviews and test generation.
- Define boundaries between agent delegation and human tasks to ensure agent success.
- Diagnose agent behavior through trace analysis and debug complex failure points.
- Deliver end-to-end AI features from prototype to production, including model selection and monitoring.
- Assess org-wide AI adoption to identify bottlenecks and evangelize best practices.
- Mentor engineers on design reviews, pairing, and critical evaluation of AI-generated code.
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