Senior Machine Learning Engineer

BJAK; Greater London; On-site; GBP 80,000 - 120,000; Full time

Job summary

BJAK is hiring a Senior Member of Technical Staff, Machine Learning, to lead the design and production of core ML systems. This hands-on role requires prior experience with ML systems, focusing on building solutions that are reliable and scalable. You will be responsible for end-to-end ownership, from data preparation to debugging complex issues in production. Collaborating closely with teams, you will mentor peers and ensure ML features meet business goals, all while maintaining high standards of reliability and performance.

Qualifications

• Experience building and shipping ML systems used in production.
• Understanding of ML models in real-world conditions.
• Strong production-quality coding skills.

Responsibilities

• Build and maintain core ML systems.
• Own end-to-end data preparation and model training.
• Debug and resolve production issues.

Skills

ML systems design

Python

PyTorch

JAX

Debugging

Mentoring

Job description

About The Role

A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat‑based applications, our product focuses on achieving high reliability for long‑running workflows, persistent context, and real‑world task completion. The system must handle multi‑step reasoning, interact with external tools, and remain reliable despite non‑deterministic model behaviour.

Role Overview

As a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale. This is a hands‑on, high‑impact role focused on depth.

Focus

• Build core ML systems that power a proactive, long‑horizon AI product.
• Own work end‑to‑end: data preparation, training, evaluation, inference, and iteration.
• Turn research ideas into working systems that run reliably in production.
• Debug model failures and system issues using real production signals.
• Iterate quickly: ship, measure outcomes, refine, and repeat.
• Collaborate closely with research, product, and engineering to deliver real user impact.
• Mentor and review work from other ML engineers through example and technical judgment.
• Work under real production constraints: latency, cost, reliability, and safety.

Tech Stack

• Python
• PyTorch / JAX
• GPU‑based training and inference systems

Ideal Experience

• You have built and shipped ML systems used by real users.
• You understand how modern ML models behave — and misbehave — in production.
• You write strong, production‑quality code and think in systems, not scripts.
• You take ownership, work independently, and push work across the finish line.
• You learn fast, communicate clearly, and improve through iteration.

Outcomes

• ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets.
• Complex production issues are monitored, debugged, and resolved with minimal disruption.
• Training, inference, and data pipelines are robust, scalable, and maintainable over time.
• Drives measurable improvements in ML systems based on real‑world signals and user feedback.
• Provides mentorship and technical guidance to peers, raising the overall ML engineering standard.
• Collaborates cross‑functionally to ensure ML features integrate seamlessly into products and meet business goals.