Agentic AI Engineer, Catapult Sports
$107.3-214.5k
OTE
Sports technology for athlete performance
Be an early applicant
Sports technology for athlete performance
501-1000 employees
Be an early applicant
$107.3-214.5k
OTE
501-1000 employees
Company mission
To unleash the potential of every athlete and team on earth.
Role
Who you are
- You have personally shipped a production agentic AI system used by real users
- You have 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
- 5+ years of professional experience in applied ML, AI or software engineering
- Strong Python
- Strong software engineering fundamentals
- Experience building and operating production systems
- 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
- You don't need to be a sports scientist, but familiarity with workload, readiness, recovery, biomechanics or athlete performance data will help
- If you can demonstrate all three of the following, we'd like to hear from you:
- Production agentic AI
- Production multi-agent orchestration
- Hands-on confidence calibration
- We don't expect every candidate to have every preferred skill. If you have the core experience and are excited by the problem, please apply
- 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!
What the job involves
- 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 synthesises conflicting outputs
- Develop systems that evaluate confidence, uncertainty and consequence before recommendations reach a practitioner
- Build 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
- Create workflows that 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
- The goal is simple: multiple specialist agents working together to answer complex performance questions with a recommendation that is fast, grounded and calibrated
- This is a senior engineering role. Three technical capabilities are essential
- We care about the difference between a model that sounds confident and a system with measurably calibrated confidence
- 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
Our take
Catapult is a leader in the sports technology market, pioneering wearable technology that provides valuable insights to help athletes prevent injuries, optimise performance and manage their return to play. Over time, the company has expanded its offerings to include advanced wearable analytics, video analysis tools, and athlete management systems.
The company boasts an elite clientele including thousands of big-name teams worldwide, such as Real Madrid, Bayern Munich, the New York Giants, and the Australian Cricket team. These teams rely on Catapult's cutting-edge technology to monitor and improve athletes' performance across training and competition.
This is a good demonstration of Catapult's value, but where it stands out is in its support of grassroots athletes and clubs. Essentially, while many consumer-grade wearables excel at collecting data, they fail to guide people in using it effectively. To address this, Catapult creates tailored training plans to help amateur athletes leverage data for better results in a similar way to professionals.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
-5% employee growth in 12 months
Company
Company HQ
Prahran, VIC
Leadership
Will Lopes
(CEO & Managing Director)Joined Catapult in 2019 as CEO and Managing Director. Previously spent 19 years at Audible in a number of roles, including Executive Vice President of International Operations and CRO. They also founded Corridor Services where they served as the CEO.
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