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 cause
Data 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
To apply or discuss this role further please send your CV to Don Fletcher via don.fletcher@ap-executive.com
AP Executive are working with a well capitalised early stage AI firm looking to expand their ML Team.