Principal Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Bjak. London · full-time · no salary stated · we estimate £125,500–£152,500.
Bjak has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Run ML systems across the full lifecycle, from preparing data and training models through evaluation and deployment
- Build pipelines for training and fine-tuning large models, plus tests for capability, safety and real-world performance
- Design inference systems that balance speed, GPU use, memory, cost and reliability
- Create data pipelines for real-world and synthetic examples, then monitor production models and improve them over time
- Work with research and application engineers to turn model capabilities into product changes and resolve production issues
What you bring
- You have shipped ML systems used in production, rather than stopping at research prototypes
- You understand large-model training, fine-tuning, evaluation and inference, including GPU-based workloads at meaningful scale
- You bring strong software and systems skills, with sound technical judgment and the ability to work through ambiguity independently
Who this fits
This is a hands-on principal-level role for an engineer who can lead execution while remaining close to the technical work. You’ll need to work independently, make trade-offs, and help other team members deliver. The role is based in London and hybrid.
From the employer
About the Role
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
As Principal Machine Learning Engineer, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.
You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product.
What You'll Own
Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment.
Build and evolve training and fine-tuning pipelines for large models.
Design evaluation systems that measure capability, robustness, safety, and real-world product performance.
Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.
Build data pipelines and systems for high-quality real-world and synthetic training data.
Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.
Partner closely with research and application engineering to turn model capabilities into product improvements.
Make pragmatic technical trade-offs and rapidly iterate based on real-world performance.
What We're Looking For
Experience building and shipping ML systems used in production, not just research prototypes.
Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.
Strong software engineering and systems fundamentals.
Experience operating ML workloads at meaningful scale, particularly GPU-based systems.
Strong technical judgment and the ability to navigate ambiguous problems independently.
A bias toward experimentation, measurement, and shipping.
High standards for correctness, reliability, and production quality.
Outcomes
Research and models reliably translate into production-ready solutions with clear performance and quality targets.
ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
Production issues are detected, debugged, and resolved quickly, minimizing user impact.
Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
Iterations on models and systems are measurable, safe, and improve user experience over time.
Tech Stack
Python
PyTorch / JAX
GPU-based training and inference system
Ideal Experience
You have built or shipped real ML systems used by people, not just demos.
You are comfortable working with large models and understanding their failure modes.
You write strong, production-grade code and care about system correctness.
How We Work
We are a small, high-talent-density, hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently.
We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.
Interview process
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.