Machine Learning Platform Engineer
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Job Description
ML Platform Engineer building training, deployment, inference, and observability infrastructure. Supporting A1’s proactive smart assistant with scalable, reliable, cost-efficient AI systems.
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 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
- Collaborate with AI engineers, researchers, and product teams to turn model requirements into production-ready infrastructure
- Ensure AI infrastructure reliably supports production workloads at scale
- Enable efficient model training, evaluation, deployment, and improvement
- Deliver inference systems with strong latency, throughput, reliability, and cost efficiency
- Ensure ML pipelines are reproducible, observable, maintainable, and robust
- Detect and diagnose model and infrastructure regressions quickly
- Create reusable ML infrastructure platform primitives
- Enable the AI stack to evolve as new models, architectures, and inference techniques emerge
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
- Experience with Python
- Experience with PyTorch and/or JAX
- Familiarity with LLM/ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
- Experience with cloud infrastructure
- Experience with distributed systems
- Experience with ML/data pipelines and workflow orchestration
- Experience with GPU infrastructure and performance tooling
- Experience with vector databases and retrieval infrastructure
- Ability to work in ambiguous, fast-moving environments
- Ownership, experimentation, and continuous improvement mindset
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