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About the Role
You'll own the AI layer's learning loop, focusing on trusted evaluations, effective retrieval on messy fleet data, and model/harness choices to reduce costs. This role involves shipping applied ML weekly.
Responsibilities
- Build evaluation systems for quality, speed, and cost.
- Make retrieval over fleet data state of the art.
- Drive inference cost down through routing, caching, and distillation.
- Train task-specific models where APIs fall short.
- Turn cross-fleet usage into datasets and detectors that improve with scale.
Requirements
- 3+ years in applied ML.
- Real LLM-systems experience including evaluations, retrieval, and fine-tuning.
- Proficiency in Python for shipping product, not just notebooks.
- A pragmatic, measurement-driven style.
Skills
- Applied ML
- LLM systems
- Evaluations
- Retrieval
- Fine-tuning
- Python
- Model training
- Embeddings
- Task models
- Time-series data
- Sensor data
- Inference cost optimization
Location
- Sydney
- US
Work Type
- Flexible working arrangements
Experience Level
- 3+ years in applied ML
Salary/Compensations
- Competitive salary
Benefits
- Equity
- Flexible working arrangements
About the Company
- Alloy is the AI-native data platform for robotics teams, turning robot fleet data into a queryable layer with AI agents.
- The company has raised $16m, backed by Square Peg, Blackbird, and Airtree, with angel investors from leading AI and robotics companies.
- Alloy partners with robotics teams across defence, agriculture, maritime, humanoids, construction, and medical sectors.
- The team is lean, based in Sydney, and is expanding with its first US hires.
- Alloy focuses on building a company that is small, fast, and serious about craft, where employees own outcomes end-to-end.