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Staff Machine Learning EngineerBJAK · United Kingdom
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BJAK

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Staff Machine Learning Engineer

🗓️ Posted 2026-10-07 United Kingdom Fulltime Hybrid ICT
💰 Salary Not shared 📊 Market 10,833 GBP–16,667 GBP/mo

About the Company

BJAK Sdn Bhd (Company No. 201901030483 (1339813-K) Our mission is to develop technology based solutions to improve financial inclusion. We develop new & innovative platforms & services globally. For example, we are the first platform to simplify and digitise comprehensive life and medical insurance, supported by AI agent. BJAK is the largest insurance platform in Southeast Asia. If you enjoy building cutting edge platform-ecosystems that gives equal access to financial services to everyone at scale, join us. Software Development Specialties: fintech

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 product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things As Staff Machine Learning Engineer, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.

Responsibilities

  • ● 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.
  • ● Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.
  • ● Strong software engineering and systems fundamentals.
  • ● 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.
  • ● 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.