AI Engineer at Genius Sports
New York, NY
$170,000–$200,000from the description
Jul 21, 2026
New York, NY
Jul 23, 2026
What this job asks for AI summary
This role involves building and owning applied AI systems that process live and historical sports data — including tracking data, video, audio, and structured feeds — to detect events, estimate probabilities, project fan interest, and automate play-by-play collection. The work spans the full ML lifecycle: dataset construction, model training, inference pipeline development, and production evaluation across multiple sports. It suits engineers with solid production ML experience who are comfortable working with messy, multimodal data and composing models, algorithms, and LLM-based components into real-time workflows.
Mid level · 3+ years · Full-time
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (9)
The role title is 'AI Engineer' with no seniority level in the title; the title states no level. However, the 3+ years requirement and scope (owning AI systems end-to-end, mentoring junior teammates) align with a Mid-level band — the mentoring responsibility is a soft signal toward Senior, but the stated minimum experience and lack of explicit senior framing keep it at Mid.
SOC classification is a genuine judgment call: the role is heavily ML/modeling-focused (data scientists, 15-2051) but also emphasizes production engineering, inference pipelines, and system design (software developers, 15-1252). 15-2051 was chosen as primary because the core day-to-day work centers on model development, empirical evaluation, and statistical/ML reasoning over sports data.
No work location city is specified in the posting. The company (Genius Sports) has US offices but the posting does not name a specific metro. The role is described as 'office-first' / hybrid, so remote=false.
LLMs and agentic AI are listed under required qualifications as 'Demonstrated interest in…' — this is firm language in the requirements block, so they are treated as hard gates despite the softer framing.
AWS Bedrock is listed under Preferred Qualifications alongside other cloud AI platforms as an example; treated as preferred.
'Union' (ML training platform) appears only under Preferred Qualifications.
Rust appears only under Preferred Qualifications ('Experience building production systems in Rust').
Streaming/event-driven data workflows are explicitly called 'a plus' under Preferred Qualifications.
Ignored 5 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): ML model training and deployment, predictive modeling, sequence modeling, multimodal modeling, streaming/event-driven data.
Read the full posting
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