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On-site
New York
Posted · 20.09.2026
Greenhouse (US)

# Agentic AI Engineer

Catapult Sports

Catapult is building the future of sports performance technology. Since 2006, we’ve helped more than 5,000 teams use data, science and technology to improve athlete health, readiness and performance. Our customers include teams across the NFL, NBA, NHL, MLS, EPL, AFL, NRL, NCAA and many more. Now we're building the next layer of that platform: AI that can reason across everything we know about an athlete and turn it into intelligence a coach or performance practitioner can trust. We're&nbsp;looking&nbsp;for&nbsp;an&nbsp;Agentic&nbsp;AI&nbsp;Engineer&nbsp;who&nbsp;has&nbsp;already&nbsp;shipped&nbsp;production&nbsp;AI&nbsp;systems&nbsp;and&nbsp;understands&nbsp;what&nbsp;it&nbsp;takes&nbsp;to&nbsp;make&nbsp;them&nbsp;reliable,&nbsp;measurable&nbsp;and&nbsp;trustworthy. &nbsp; What you'll do: Design&nbsp;and&nbsp;ship&nbsp;specialist&nbsp;AI&nbsp;agents&nbsp;that&nbsp;use&nbsp;memory,&nbsp;tools,&nbsp;data&nbsp;and&nbsp;multi-step&nbsp;reasoning. Build&nbsp;multi-agent&nbsp;orchestration&nbsp;that&nbsp;routes&nbsp;work&nbsp;between&nbsp;specialist&nbsp;agents,&nbsp;manages&nbsp;dependencies&nbsp;and&nbsp;synthesises&nbsp;conflicting&nbsp;outputs. Develop&nbsp;systems&nbsp;that&nbsp;evaluate&nbsp;confidence,&nbsp;uncertainty&nbsp;and&nbsp;consequence&nbsp;before&nbsp;recommendations&nbsp;reach&nbsp;a&nbsp;practitioner. Build&nbsp;human-in-the-loop&nbsp;escalation&nbsp;so&nbsp;the&nbsp;system&nbsp;knows&nbsp;when&nbsp;to&nbsp;answer,&nbsp;when&nbsp;to&nbsp;ask&nbsp;for&nbsp;more&nbsp;information&nbsp;and&nbsp;when&nbsp;to&nbsp;defer&nbsp;to&nbsp;a&nbsp;human. Create&nbsp;workflows&nbsp;that&nbsp;turn&nbsp;sport&nbsp;scientist&nbsp;expertise&nbsp;into&nbsp;validated,&nbsp;versioned&nbsp;and&nbsp;testable&nbsp;agent&nbsp;capabilities. Build&nbsp;evaluation,&nbsp;observability&nbsp;and&nbsp;regression&nbsp;testing&nbsp;so&nbsp;agent&nbsp;performance&nbsp;can&nbsp;be&nbsp;measured&nbsp;and&nbsp;improved&nbsp;in&nbsp;production. Work&nbsp;with&nbsp;domain&nbsp;experts&nbsp;to&nbsp;ensure&nbsp;AI&nbsp;outputs&nbsp;are&nbsp;grounded,&nbsp;traceable&nbsp;and&nbsp;actionable. The goal is simple: multiple specialist agents working together to answer complex performance questions with a recommendation that is fast, grounded and calibrated. What you need: This is a senior engineering role. Three technical capabilities are essential. 1. Production agentic AI You have personally shipped a production agentic AI system used by real users. You have hands-on experience with: Memory&nbsp;or&nbsp;persistent&nbsp;state Tool&nbsp;use&nbsp;or&nbsp;tool&nbsp;calling Multi-step&nbsp;reasoning&nbsp;or&nbsp;workflows Production&nbsp;deployment&nbsp;and&nbsp;operation Chatbots, prompt engineering and RAG alone are not enough. 2. Multi-agent orchestration You&nbsp;have&nbsp;built&nbsp;or&nbsp;substantially&nbsp;contributed&nbsp;to&nbsp;a&nbsp;production&nbsp;multi-agent&nbsp;system. You understand: Agent&nbsp;routing&nbsp;and&nbsp;orchestration Specialist&nbsp;agent&nbsp;composition Dependency-aware&nbsp;workflows Parallel&nbsp;and&nbsp;sequential&nbsp;execution Conflicting&nbsp;agent&nbsp;outputs Response&nbsp;synthesis Experience&nbsp;with&nbsp;LangGraph,&nbsp;AutoGen,&nbsp;CrewAI&nbsp;or&nbsp;equivalent&nbsp;frameworks is valuable. 3. Confidence calibration You have hands-on experience calibrating probabilistic ML or AI systems. You should be comfortable with: Platt&nbsp;scaling Isotonic&nbsp;regression Expected&nbsp;Calibration&nbsp;Error&nbsp;(ECE) Reliability&nbsp;and&nbsp;calibration&nbsp;curves Confidence&nbsp;and&nbsp;uncertainty&nbsp;estimation We&nbsp;care&nbsp;about&nbsp;the&nbsp;difference&nbsp;between&nbsp;a&nbsp;model&nbsp;that&nbsp;sounds&nbsp;confident&nbsp;and&nbsp;a&nbsp;system&nbsp;with&nbsp;measurably&nbsp;calibrated&nbsp;confidence. &nbsp; You should also have: 5+&nbsp;years&nbsp;of&nbsp;professional&nbsp;experience&nbsp;in&nbsp;applied&nbsp;ML,&nbsp;AI&nbsp;or&nbsp;software&nbsp;engineering Strong&nbsp;Python Strong&nbsp;software&nbsp;engineering&nbsp;fundamentals Experience&nbsp;building&nbsp;and&nbsp;operating&nbsp;production&nbsp;systems Experience with: Production&nbsp;RAG&nbsp;and&nbsp;reranking Foundation-model&nbsp;fine-tuning&nbsp;or&nbsp;domain&nbsp;adaptation LoRA,&nbsp;PEFT&nbsp;or&nbsp;similar&nbsp;techniques LLM&nbsp;observability&nbsp;and&nbsp;drift&nbsp;detection Evaluation&nbsp;harnesses&nbsp;and&nbsp;automated&nbsp;regression&nbsp;testing Human-in-the-loop&nbsp;architectures Confidence&nbsp;thresholds&nbsp;and&nbsp;escalation&nbsp;models Causal&nbsp;or&nbsp;counterfactual&nbsp;reasoning Go/Golang AWS,&nbsp;including&nbsp;ECS,&nbsp;EC2,&nbsp;Lambda,&nbsp;SNS&nbsp;or&nbsp;SQS GraphQL,&nbsp;REST&nbsp;or&nbsp;gRPC PostgreSQL&nbsp;or&nbsp;MongoDB Experience working with sport scientists, clinicians or other domain experts is a plus. You don't need to be a sports scientist, but familiarity with workload, readiness, recovery, biomechanics or athlete performance data will help. &nbsp; What success looks like: You'll help build a platform where specialist agents can investigate complex performance questions, use the right evidence, assess their uncertainty and produce a recommendation that a practitioner can understand and trust. Most importantly, the system will know when not to answer. Every recommendation should be: Grounded.&nbsp;Calibrated.&nbsp;Traceable.&nbsp;Escalation-aware. The practitioner remains responsible for the decision. Your job is to make that decision better informed, faster and more defensible. &nbsp; Before you apply: If you can demonstrate all three of the following, we'd like to hear from you: Production&nbsp;agentic&nbsp;AI Production&nbsp;multi-agent&nbsp;orchestration Hands-on&nbsp;confidence&nbsp;calibration We&nbsp;don't&nbsp;expect&nbsp;every&nbsp;candidate&nbsp;to&nbsp;have&nbsp;every&nbsp;preferred&nbsp;skill.&nbsp;If&nbsp;you&nbsp;have&nbsp;the&nbsp;core&nbsp;experience&nbsp;and&nbsp;are&nbsp;excited&nbsp;by&nbsp;the&nbsp;problem,&nbsp;please&nbsp;apply. &nbsp; Compensation &amp; Benefits The target Total Compensation range for this position is $107,250 - $214,500 per year. This range is inclusive of base salary and a target incentive plan (which may include equity, commission, or other bonus structures). Your specific compensation within this range will be determined by factors such as your geographic location, relevant experience, and job-related skills. In addition to this compensation, Catapult also offers generous paid leave and recognized company holidays, and the opportunity to participate in our comprehensive benefits package, including Health, Dental, and Vision insurance, and 401(k) retirement plan with company match. &nbsp; Whether you’re interested in sports or not, you’ll have the satisfaction of knowing your work is supporting some of the most successful teams and athletes on the planet!&nbsp; Research shows that while men apply for jobs when they meet an average of 60% of the criteria, women and other marginalized groups tend only to apply when they check every box. So if you have what it takes, but don't meet every single point in our job ad, please still get in touch! We would love to have a chat and see if you could be a great addition to our team. We are building the future of sports performance. Our priority is to find the brightest talent who can add to our team culture, actively contribute, and be excited about what they do. &nbsp; All offers of employment are subject to Catapult's positive prehire check. To find out more, please contact the Talent Partner for this role.

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