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About the Role

Catapult is seeking an Agentic AI Engineer to build the AI layer that compounds all measured data, aiming to become the indispensable intelligence partner for every coach and athlete. This role involves designing and building specialist agents, a workflow engine for domain scientist expertise, and a decision intelligence layer for trustworthy recommendations.

Responsibilities

• Design and build specialist agents that form the AI bench.
• Develop the workflow engine that encodes domain scientist expertise into validated agent skills at scale.
• Create the decision intelligence layer that transforms agent outputs into calibrated, escalation-aware recommendations.
• Build agentic systems with memory, tool use, multi-step reasoning, and calibrated outputs.
• Implement confidence calibration and evaluation frameworks for probabilistic systems.
• Build production RAG pipelines with reranking.
• Fine-tune or adapt foundation models for specific domains.
• Implement LLM observability and drift detection in production.
• Build knowledge acquisition workflows for domain-specific AI.
• Encode expert knowledge into AI systems by translating judgment into calibration signals.
• Develop human-in-the-loop architectures, including escalation models, confidence thresholds, and consequence classification.
• Build systems that know when they do not know and what to do about it, ensuring human escalation is a first-class architectural principle.
• Design the knowledge acquisition engine to encode domain scientist expertise into validated, versioned, production-ready agent skills.
• Take proof-of-concept agents and make them production-ready, calibrated, defensible, and compounding in value.

Requirements

• 5+ years in applied ML or AI engineering.
• At least 2 years building production agentic AI systems (not chatbots or RAG pipelines alone).
• Systems must have memory, tool use, multi-step reasoning, and calibrated outputs.
• Deep experience with multi-agent frameworks and orchestration.
• Experience with dependency-aware routing, specialist agent composition, and response synthesis across conflicting outputs.
• Hands-on experience with confidence calibration and evaluation frameworks for probabilistic systems.
• Experience building evaluation harnesses that run against full input distributions.
• Production RAG experience with reranking.
• Experience fine-tuning or adapting foundation models for specific domains.
• Strong Python and Golang skills.
• Experience with LLM observability and drift detection in production.
• Experience building knowledge acquisition workflows for domain-specific AI.
• Experience working with domain scientists or clinical practitioners to encode expert knowledge into AI systems.
• Experience with human-in-the-loop architectures.
• Familiarity with sports science, biomechanics, or performance data.
• Experience with causal or counterfactual reasoning in AI systems.
• Experience working with AWS (ECS, EC2, Lambda, SNS, SQS, etc), GraphQL, REST, gRPC, Postgres, Mongo.
• Ability to build a system that knows when it does not know and what to do about it.
• Ability to make the promise of practitioner ownership of decisions architecturally real.
• Ability to encode domain scientist expertise into validated, versioned, production-ready agent skills at speed and scale.
• Ability to take proof-of-concept agents and make them production-ready, calibrated, defensible, and compounding in value.

Skills

• Agentic AI systems
• Memory
• Tool use
• Multi-step reasoning
• Calibrated outputs
• Multi-agent frameworks
• Orchestration
• Dependency-aware routing
• Specialist agent composition
• Response synthesis
• Confidence calibration
• Evaluation frameworks for probabilistic systems
• Platt scaling
• Isotonic regression
• ECE
• Evaluation harnesses
• Production RAG
• Reranking
• Retrieval quality optimization
• Foundation model adaptation
• Knowledge injection
• Python
• Golang
• LLM observability
• Drift detection
• Knowledge acquisition workflows
• Annotation interfaces
• Version-controlled knowledge bases
• Review queues
• Regression testing
• Human-in-the-loop architectures
• Escalation models
• Confidence thresholds
• Consequence classification
• Sports science
• Biomechanics
• Performance data
• Causal reasoning
• Counterfactual reasoning
• AWS (ECS, EC2, Lambda, SNS, SQS)
• GraphQL
• REST
• gRPC
• Postgres
• Mongo

Location

• Remote

Work Type

• Full-time

Experience Level

• Mid-level
• Senior

Salary/Compensations

• $107,250 - $214,500 per year

Benefits

• Generous paid leave
• Recognized company holidays
• Comprehensive benefits package
• Health insurance
• Dental insurance
• Vision insurance
• 401(k) retirement plan with company match

About the Company

• Catapult is building the future of sports performance technology, with a mission to Unleash the Potential of every athlete and team on earth.
• Catapult's solutions have been leading the way in sports performance software, science, and data since 2006.
• Catapult works with over 5,000+ teams globally, empowering coaches, managers, and trainers in premier teams across various leagues.
• Catapult provides information to optimize athletes’ health, game-day readiness, performance, and in-game tactics.
• Catapult is a sports technology company that empowers professional teams to make data-driven decisions.
• Catapult delivers health, performance, video, and AI insights from the locker room to competitive environments.
• Catapult has spent twenty years collecting ground-truth athlete data from hardware on the body and on the field, across 40+ sports and 100+ countries.
• The CEO has made the AI platform the central strategic bet for the next chapter of the company.

Equal Opportunity

• 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.
• All offers of employment are subject to Catapult's positive prehire check.