Remote Staff Machine Learning Engineer

Salary: £29,000 - 69,000 per year

Requirements

• We have built or shipped real ML systems used by people, not just demos.
• We are comfortable working with large models and understanding their failure modes.
• We write strong, production-grade code and care about system correctness.
• We are self-directed, pragmatic, and take full ownership of outcomes.
• We communicate clearly and collaborate well in small, high-trust teams.
• We have experience with Python.
• We have experience with PyTorch or JAX.
• We have experience with GPU-based training and inference systems.

Responsibilities

• We own end-to-end ML system execution, including data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• We fine-tune and adapt models using methods such as LoRA, QLoRA, SFT, DPO, and distillation.
• We architect and operate scalable inference systems, balancing latency, cost, and reliability.
• We design and maintain data systems for high-quality synthetic and real-world training data.
• We implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
• We own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• We collaborate closely with application engineering to integrate ML systems into backend, mobile, and desktop products.
• We make pragmatic trade-offs and ship improvements quickly, learning from real usage.
• We work under real production constraints, including latency, cost, reliability, and safety.

Technologies

• AI
• Architect
• Backend
• Mobile
• PyTorch
• Python
• Machine Learning

More

We are building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising, and workflows. Our product is designed for high reliability, persistent context, multi-step reasoning, external tool use, and real-world task completion. We are a high-talent-density, hands-on team that makes decisions collectively, moves quickly, and balances shipping high-quality work with learning. We value structure, judgment, independence, transparency, and efficiency, and our interview process typically includes 3 to 4 virtual and/or onsite interviews with a prompt decision.

last updated 28 week of 2026