Agentic AI Engineer

About the role

Catapult is building the next layer of sports performance technology: 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 looking for an Agentic AI Engineer who has already shipped production AI systems and understands what it takes to make them reliable, measurable, and trustworthy.

The goal is simple: multiple specialist agents working together to answer complex performance questions with a recommendation that is fast, grounded, and calibrated.

Responsibilities

• Design and ship specialist AI agents that use memory, tools, data, and multi-step reasoning.
• Build multi-agent orchestration that routes work between specialist agents, manages dependencies, and synthesizes conflicting outputs.
• Develop systems that evaluate confidence, uncertainty, and consequence before recommendations reach a practitioner.
• Create human-in-the-loop escalation so the system knows when to answer, when to ask for more information, and when to defer to a human.
• Turn sport scientist expertise into validated, versioned, and testable agent capabilities.
• Build evaluation, observability, and regression testing so agent performance can be measured and improved in production.
• Work with domain experts to ensure AI outputs are grounded, traceable, and actionable.

Requirements

This is a senior engineering role. Three technical capabilities are essential:

• Production agentic AI
  • You have personally shipped a production agentic AI system used by real users.
  • Hands-on experience with:
    • Memory or persistent state
    • Tool use or tool calling
    • Multi-step reasoning or workflows
    • Production deployment and operation
  • Chatbots, prompt engineering, and RAG alone are not enough.
• Multi-agent orchestration
  • You have built or substantially contributed to a production multi-agent system.
  • You understand:
    • Agent routing and orchestration
    • Specialist agent composition
    • Dependency-aware workflows
    • Parallel and sequential execution
    • Conflicting agent outputs
    • Response synthesis
  • Experience with LangGraph, AutoGen, CrewAI, or equivalent frameworks is valuable.
• Confidence calibration
  • You have hands-on experience calibrating probabilistic ML or AI systems.
  • You should be comfortable with:
    • Platt scaling
    • Isotonic regression
    • Expected Calibration Error (ECE)
    • Reliability and calibration curves
    • Confidence and uncertainty estimation

You should also have:

• 5+ years of professional experience in applied ML, AI, or software engineering
• Strong Python
• Strong software engineering fundamentals
• Experience building and operating production systems

Skills

Experience with:

• Production RAG and reranking
• Foundation-model fine-tuning or domain adaptation
• LoRA, PEFT, or similar techniques
• LLM observability and drift detection
• Evaluation harnesses and automated regression testing
• Human-in-the-loop architectures
• Confidence thresholds and escalation models
• Causal or counterfactual reasoning
• Go/Golang
• AWS, including ECS, EC2, Lambda, SNS, or SQS
• GraphQL, REST, or gRPC
• PostgreSQL or MongoDB

Experience working with sport scientists, clinicians, or other domain experts is a plus. Familiarity with workload, readiness, recovery, biomechanics, or athlete performance data will help.

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
• Calibrated
• Traceable
• Escalation-aware

The practitioner remains responsible for the decision. Your job is to make that decision better informed, faster, and more defensible.

About Catapult

We've spent twenty years collecting ground-truth athlete data across more than 40 sports and 100+ countries, alongside deep relationships with the scientists, coaches, and performance practitioners who understand what that data means. Now we're bringing that data, expertise, and AI together. This is an opportunity to build the intelligence layer on top of one of the world's richest sources of sports performance data and help define what trustworthy agentic AI looks like in a real-world, high-impact environment.

Pay

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.

Benefits

• Generous paid leave and recognized company holidays
• Comprehensive benefits package, including:
  • Health, Dental, and Vision insurance
  • 401(k) retirement plan with company match

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!