Machine Learning Platform Engineer

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they’re not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.Our objective is to organise anyone’s life, allowing us all to spend time on valuable and meaningful things.About the RoleAs an ML Platform Engineer, you will build the infrastructure and systems that power ActAI’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 infrastructurePythonPyTorch / 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 emerge#J-*****-Ljbffr

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

Lleida, España

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they’re not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations. Our objective is to organise anyone’s life, allowing us all to spend time on valuable and meaningful things.