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Lai Chi Kok, Hong Kong
About The Role
You are the bridge between raw data and robotic intelligence. As a Full Stack Engineer, ML Data & Evals, you will build the "Laboratory" where our ML team evaluates and deploys models. Your work accelerates the research-to-production loop, creating the infrastructure to launch on-robot evaluations and visualize model performance in complex, real-world scenarios.
Key Responsibilities
- Interactive Data Systems: Architect the interfaces and engines that unify robot and Skill Capture Gloves™ data, enabling "human-in-the-loop" workflows, from episode annotation to seamless switching between autonomous execution and manual teleoperation intervention.
- Evaluation & Benchmarking: Develop high-performance tools to compare model-driven motion against human-captured ground truth, helping quantify model progress across diverse tasks.
- Data Processing & Orchestration: Architect processing services that transform raw captures and sensor data into ML-ready formats, ensuring a seamless flow from our global collection systems to the models that power our robots.
- Startup Fluidity: While this role focuses on the ML Platform, our Full Stack Engineers are comfortable shifting priorities and domains, eager to jump into other parts of the stack as we scale.
Qualifications
- Full-Stack Proficiency: 3+ years of building and scaling cloud-native applications with mastery of modern frontend (e.g., React, TypeScript) and robust backend (e.g., Node.js, Python) stacks.
- Operational Mindset: Experience building high-throughput internal tooling or "human-in-the-loop" platforms.
- Interactive ML Observability: A high bar for building low-friction interfaces that make complex model behaviors, sensor data, and "human-in-the-loop" interventions easy to interpret and act upon.
Nice to Have
- Experience as a founding or early hire; able to define roadmaps where no blueprint exists.
- Experience building robust ETL pipelines that transform terabytes of multimodal data into structured, high-quality datasets.
- Familiarity with tools like Weights & Biases, MLFlow, or similar experiment tracking frameworks.
Even if you don’t meet every single requirement, we encourage you to apply. Studies show that women and underrepresented groups often hold back unless they meet 100% of the criteria — we don’t want that to be the reason we miss out on great talent.
Application
Interested parties please send detailed resume and expected salary to hr@axonex.ai
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