Machine Learning Platform Engineer

Overview

Build and operate the infrastructure and systems powering AI products, covering model training, evaluation, deployment, inference, observability, and continuous improvement.

What you'll do

• Build and operate ML infrastructure and platforms for AI products.
• Design systems for model training, evaluation, deployment, inference, and experimentation.
• Optimise model-serving infrastructure for high-throughput and low-latency workloads.
• Develop pipelines for data preparation, training, evaluation, model release, and continuous improvement.
• Build evaluation, benchmarking, monitoring, tracing, and alerting infrastructure.
• Identify ML-stack bottlenecks and improve reliability, scalability, latency, throughput, and cost.
• Partner with AI engineers, researchers, and product teams on production-ready infrastructure.

What you'll need

• Strong software engineering fundamentals and production-systems experience.
• Experience building ML infrastructure, platforms, or production machine learning systems.
• Experience with model deployment, inference, evaluation, or data pipelines.
• Strong understanding of distributed systems and system reliability.
• Ability to write clean, maintainable, production-quality code.
• Python experience.
• Familiarity with PyTorch or JAX, cloud infrastructure, and ML/data pipelines.

Details

• Location: United States.