Machine Learning Engineer

We’re looking for an experienced Machine Learning Engineer to lead the development and training of advanced large-scale language models. In this role, you will be responsible for pushing the performance and reliability of next-generation AI systems, specifically focusing on models that assist with complex real-world tasks. You’ll work closely with cross-functional teams including infrastructure, product and research to shape both the training pipeline and the evaluation of highly capable models.

Key Responsibilities

• Design and execute large-scale training experiments on multi-GPU and distributed environments using cutting-edge ML frameworks.
• Lead both supervised fine-tuning (SFT) and reinforcement learning (RL) workflows to improve model performance on domain-specific tasks.
• Build, maintain, and optimise custom training pipelines, including dataset preparation, distributed training primitives, and scheduling of multi-node jobs.
• Collaborate across engineering and research teams to align training goals with product priorities and performance metrics.
• Troubleshoot training challenges such as stability, scaling issues, and GPU utilisation bottlenecks.

What We’re Looking For

• Experience:
  
  3–5+ years working in ML engineering or applied machine learning roles, with hands‑on responsibility for training and deploying models in production‑like environments.
• Technical
  
  Skills:
• Strong proficiency with PyTorch including distributed training (e.g., DDP/FSDP).
• Practical experience training large sequence models or transformer-based architectures.
• Comfortable building and maintaining data pipelines, optimising large datasets, and handling model scaling challenges.
• Solid software engineering fundamentals — clean, maintainable code and version control best practices.
• System Knowledge:
  Hands‑on experience with multi-node GPU clusters, orchestration tools (e.g., Kubernetes, Slurm) and performance tuning.
• Communication:
  Clear and effective communicator, able to share insights with both technical and non-technical stakeholders.
• Experience with reinforcement learning workflows and sequence-level reward strategies.
• Familiarity with model evaluation tools and benchmarks for large-scale AI systems.
• A proactive, collaborative mindset that thrives in a fast-moving environment where innovation and experimentation are central.