Machine Learning Platform Engineer
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Job Description
ML Platform Engineer building infrastructure for A1’s proactive smart assistant. Enabling reliable model training, deployment, inference, observability, and continuous improvement.
Responsibilities:
- 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 pipelines for data preparation, training, evaluation, model release, and continuous improvement
- Build platforms and tooling enabling 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
- Collaborate with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure
Requirements:
- 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
- Python proficiency
- Experience with PyTorch or JAX
- Experience with LLM/ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
- Knowledge of cloud infrastructure, distributed systems, ML/data pipelines, workflow orchestration, GPU infrastructure, performance tooling, vector databases, and retrieval infrastructure
- Comfortable working in ambiguous, fast-moving environments
- Bias toward ownership, experimentation, and continuous improvement
- Professional/fluent English proficiency for technical communication
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