Machine Learning Platform Engineer

You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.

Focus

Build and operate the ML infrastructure and platforms powering A1’s AI products

Design systems for model training, evaluation, deployment, inference, and experimentation

Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads

Improve reliability, scalability, latency, and cost efficiency of AI systems

Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement

Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster

Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions

Build production observability, monitoring, tracing, and alerting for AI/ML workloads

Improve AI systems across reliability, scalability, latency, throughput, and cost

Identify bottlenecks across the ML stack and continuously improve system performance

Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure

Tech Stack

Python

PyTorch / JAX

LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM

Cloud infrastructure

Distributed systems

ML/data pipelines and workflow orchestration

GPU infrastructure and performance tooling

Vector databases and retrieval infrastructure

Ideal Experience

Strong software engineering fundamentals and experience building production systems

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

Comfortable working in ambiguous, fast-moving environments

Bias toward ownership, experimentation, and continuous improvement

Outcomes

AI infrastructure reliably supports production workloads at scale

Models can be trained, evaluated, deployed, and improved efficiently

Inference systems deliver strong latency, throughput, reliability, and cost efficiency

ML pipelines are reproducible, observable, maintainable, and robust

Model and infrastructure regressions are detected quickly and diagnosed efficiently

Common ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product

The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge

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