About this role
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About A1There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.
Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.
About the Role
As an ML Platform Engineer, you will build the infrastructure and systems that power A1's AI capabilities.You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement.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.FocusBuild and operate the ML infrastructure and platforms powering A1’s AI productsDesign systems for model training, evaluation, deployment, inference, and experimentationBuild and optimise model serving and inference infrastructure for high-throughput and low-latency workloadsImprove reliability, scalability, latency, and cost efficiency of AI systemsDevelop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvementBuild platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models fasterDevelop evaluation and benchmarking infrastructure to measure model quality, performance, and regressionsBuild production observability, monitoring, tracing, and alerting for AI/ML workloadsImprove AI systems across reliability, scalability, latency, throughput, and costIdentify bottlenecks across the ML stack and continuously improve system performanceWork closely with AI engineers, researchers, and product teams to turn evolving model
Requirements
into production-ready infrastructureTech StackPythonPyTorch / JAXLLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLMCloud infrastructureDistributed systemsML/data pipelines and workflow orchestrationGPU infrastructure and performance toolingVector databases and retrieval infrastructureIdeal ExperienceStrong software engineering fundamentals and experience building production systemsExperience building ML infrastructure, platforms, or production machine learning systemsExperience with model deployment, inference, evaluation, or data pipelinesStrong understanding of distributed systems and system reliabilityAbility to write clean, maintainable, production-quality codeComfortable working in ambiguous, fast-moving environmentsBias toward ownership, experimentation, and continuous improvementOutcomesAI infrastructure reliably supports production workloads at scaleModels can be trained, evaluated, deployed, and improved efficientlyInference systems deliver strong latency, throughput, reliability, and cost efficiencyML pipelines are reproducible, observable, maintainable, and robustModel and infrastructure regressions are detected quickly and diagnosed efficientlyCommon ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI productThe AI stack can evolve rapidly as new models, architectures, and inference techniques emergeFind Jobs in United Kingdom on Arbeitnow
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