Senior Software Engineer, Sports AI

Genius Sports•Los Angeles, CA

23h•Hybrid

About The Position

We’re looking for a Senior Software Engineer on our Sports AI team to help build the next generation of real-time AI systems powering sports analysis and insights. These systems transform live sports broadcasts (incorporating signals from video, crowd noise, audio commentary, and text) into a structured understanding of game context and auto-generated insights. The outputs from these systems power a range of products including automated highlight clipping, augmented broadcasts, semi-automatic play-by-play collection, and natural-language insight generation. For example, these systems can detect a goal, attribute it to the correct player, and localize where it occurred on the pitch within seconds by combining crowd noise spikes, commentary signals, and video cues. This role sits at the intersection of streaming and distributed systems, AI, and product engineering. You’ll build and operate real-time pipelines that process noisy, asynchronous inputs under tight latency constraints. These systems combine traditional streaming and data processing techniques with modern multimodal AI models to produce reliable outputs in production. You’ll work on challenges like aligning signals across multiple sources, handling uncertainty and inconsistency in model outputs, and designing systems that degrade gracefully in real-world conditions. We are early in building these capabilities, so the role involves working through ambiguity, experimenting with different approaches, and building new systems from first principles where established patterns don’t yet exist.

Requirements

5+ years of experience building production-grade software systems
Strong software engineering fundamentals including system design, testing, observability, and performance
Experience building streaming, data-intensive, and/or distributed systems with real-time constraints
Proven ability to design scalable systems that are robust under real-world conditions, including handling partial failures, inconsistent data, and latency tradeoffs
Ability to evaluate system quality beyond traditional metrics (e.g., ambiguous or probabilistic outputs)
Product-minded: able to translate ambiguous problems into practical, high-impact solutions
Strong, demonstrated interest in building AI-powered features or systems; keep up with latest generative AI progress and capabilities
Comfortable working in fast-moving, iterative environments with evolving requirements

Nice To Haves

Hands-on experience integrating LLMs and other ML/AI systems in production workflows via platforms like Vertex AI or AWS Bedrock
Experience building or maintaining human-in-the-loop workflows
Familiarity with streaming systems and tooling (e.g., Pulsar, Kafka, Flink, etc.)
Experience working with audio/video streaming and processing systems, including familiarity with media delivery protocols (e.g., HLS, DASH, RTP), container formats, codecs, and tooling such as FFmpeg or GStreamer
Background or strong interest in sports
Experience with Rust

Responsibilities

Design, build, and operate real-time streaming systems that convert live sports broadcasts into structured events, attributes, and insights
Ingest and synchronize signals from video, audio, commentary, and data feeds under real-world latency and reliability constraints
Integrate modern AI models into production systems, including multi-step pipelines where model outputs are composed, validated, and refined
Design and implement evaluation and observability frameworks to measure system quality, including offline benchmarks, real-time metrics, and regression testing
Develop mechanisms for identifying and handling uncertainty in model outputs, such as confidence scoring, validation checks, and human-in-the-loop workflows where appropriate
Own systems end-to-end from early prototypes through production, making key architectural decisions around scalability, reliability, and performance
Collaborate closely with product and data partners to shape, prioritize, and ship high-impact systems
Mentor engineers and help establish best practices for building and operating applied AI systems in production