[Hiring] Machine Learning Platform Engineer @Bjak

Role Description

As an ML Platform Engineer, you will build the infrastructure and systems that power A1's AI capabilities.

• Design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement.
• Work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems.
• 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.
• 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.