As an ML Engineer, you’ll build the infrastructure behind the company’s AI capabilities—from model training and evaluation to deployment, inference, observability, and continuous improvement.

You’ll work closely with AI engineers, researchers, and product engineers to turn evolving models into reliable, scalable, and cost-efficient production systems.

What you’ll work on• Build and operate production ML infrastructure• Design systems for model training, evaluation, deployment, inference, and experimentation• Optimise model serving for high-throughput, low-latency workloads• Develop reliable data, training, evaluation, and model-release pipelines• Build evaluation and benchmarking infrastructure to measure quality and detect regressions• Implement monitoring, tracing, observability, and alerting for AI/ML workloads• Improve reliability, scalability, latency, throughput, and cost• Identify bottlenecks across the ML stack and continuously improve performance• Create reusable platforms and tooling that help engineers and researchers ship faster

Tech stack• Python• PyTorch and/or JAX• vLLM, SGLang, TensorRT-LLM, or similar serving infrastructure• Cloud and GPU infrastructure• Distributed systems• ML and data pipelines• Workflow orchestration• Vector databases and retrieval infrastructure

You may be a good fit if you have• Strong software engineering fundamentals• Experience building ML infrastructure, platforms, or production machine learning systems• Hands-on experience with model deployment, inference, evaluation, or data pipelines• A strong understanding of distributed systems and reliability• The ability to write clean, maintainable, production-quality code• Comfort working in an ambitious, fast-moving environment• A strong sense of ownership and a bias towards continuous improvement