About the Opportunity
Our client is a well‑capitalised, early‑stage technology company developing an advanced AI‑driven product for consumers. The engineering challenge is significant: the system must perform complex, multi‑step reasoning, maintain context over extended interactions, and operate reliably in production despite the inherent unpredictability of large models.
The organisation is deliberately lean, a small group of senior, high‑calibre engineers who move quickly, make decisions collectively, and hold a high bar for both quality and pace. The mission is to deliver a product experience that feels genuinely different from what’s currently on the market.
The Roles
Our client is looking to hire multiple profiles into their ML Technical staff. As a Member of Technical Staff, Machine Learning, you will build core ML components and work directly on production systems from day one, gaining first‑hand exposure to how large‑scale ML behaves outside a research setting. This role suits engineers who want to build strong systems judgement through shipping, debugging, and iterating on real‑world ML, alongside more senior colleagues.
Focus Areas
- Build and improve ML components spanning data, training, evaluation, and inference
- Fine‑tune and adapt models as part of larger production systems
- Implement evaluation and testing frameworks to understand model behaviour
- Contribute to data pipelines covering both real‑world and synthetic data
- Debug model issues, performance problems, and production incidents
- Ship improvements iteratively, guided by real user feedback
- Work closely with senior ML engineers and product teams
- Operate comfortably within the constraints of a live production system: latency, cost, reliability, and safety all matter simultaneously
What Good Looks Like in This Role
- Production ML models meet expected accuracy, latency, and reliability targets
- Production issues are identified quickly, debugged effectively, and resolved at the root causeData pipelines, training loops, and inference systems are robust, reproducible, and maintainable
- Works effectively across engineering, product, and research to deliver reliable ML‑powered features
- Improvements to models and systems are driven by real‑world signals and measurable outcomes
Technical Environment
- Python
- PyTorch / JAX
- Production ML systems running on GPU infrastructure
Candidate Profile
- Strong foundations in machine learning and modern neural network architectures
- Some hands‑on experience training, fine‑tuning, or deploying ML models
- Comfortable writing production‑quality code and picking up new tools quickly
- Curious, coachable, and keen to learn from real systems in production
- Able to work through ambiguity with guidance, growing ownership over time
- A natural bias toward shipping, iteration, and continuous improvement
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