机器学习平台工程师，机器学习（ML）和人工智能
Machine Learning Platform Engineer, Machine Learning (ML) and Artificial Intelli
机器学习平台工程师，机器学习（ML）和人工智能（AI）要求，远程办公
作为机器学习平台工程师，你将构建支持人工智能（AI）功能的基础设施和系统。你将设计并运营人工智能（AI）堆栈背后的系统，从模型训练和评估到部署、推理、可观测性和持续改进。你将与人工智能（AI）工程师、研究人员和产品工程师紧密合作，将模型转化为可靠、可扩展且成本高效的生产系统。你将构建平台、工具和基础设施，使团队能够快速实验，并有信心将AI功能投入生产。需要具备机器学习（ML）和人工智能（AI）经验。该职位为100%远程办公。
必须愿意参加一个60分钟的编程评估。
机器学习平台工程师职责：
- 构建和运营支持人工智能（AI）产品的机器学习（ML）基础设施和平台。
- 设计用于模型训练、评估、部署、推理和实验的系统。
- 构建和优化适用于高吞吐量和低延迟工作负载的模型服务和推理基础设施。
- 提升人工智能（AI）系统的可靠性、可扩展性、延迟和成本效率。
- 开发可靠的数据准备、训练、评估、模型发布和持续改进的管道。
- 构建平台和工具，使人工智能（AI）工程师和研究人员能够更快地进行实验、评估和发布模型。
- 开发评估和基准测试基础设施，以衡量模型质量、性能和退化情况。
- 构建人工智能（AI）/机器学习（ML）工作负载的生产可观测性、监控、追踪和警报。
- 在可靠性、可扩展性、延迟、吞吐量和成本方面提升人工智能（AI）系统。
- 识别机器学习（ML）堆栈中的瓶颈，并持续改进系统性能。
- 与人工智能（AI）工程师、研究人员和产品团队紧密合作，将不断变化的模型需求转化为生产就绪的基础设施。
- 机器学习平台工程师成果
- AI基础设施可靠地支持大规模生产工作负载。
- 模型可以被训练、评估、部署、
查看英文原文
Machine Learning Platform Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home
As the Machine Learning Platform Engineer, you will build the infrastructure and systems that power Artificial Intelligence (AI) capabilities. You will design and operate the systems behind the Artificial Intelligence (AI) stack, from model training and evaluation to deployment, inference, observability, and continuous improvement. You will work closely with Artificial Intelligence (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. Machine Learning (ML) and Artificial Intelligence (AI) experience are required. This position is 100% Remote.
MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT.
Machine Learning Platform Engineer Responsibilities:
- Build and operate the Machine Learning (ML) infrastructure and platforms powering Artificial Intelligence (AI) products.
- Design systems for model training, evaluation, deployment, inference, and experimentation.
- Build and optimize model serving and inference infrastructure for high-throughput and low-latency workloads.
- Improve reliability, scalability, latency, and cost efficiency of Artificial Intelligence (AI) systems.
- Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement.
- Build platforms and tooling that enable Artificial Intelligence (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 Artificial Intelligence (AI)/Machine Learning (ML) workloads.
- Improve Artificial Intelligence (AI) systems across reliability, scalability, latency, throughput, and cost.
- Identify bottlenecks across the Machine Learning (ML) stack and continuously improve system performance.
- Work closely with Artificial Intelligence (AI) engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure.
Machine Learning Platform Engineer 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.
- Machine Learning (ML) pipelines are reproducible, observable, maintainable, and robust.
- Model and infrastructure regressions are detected quickly and diagnosed efficiently.
- Common Machine Learning (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.
Machine Learning Platform Engineer Qualifications:
- Machine Learning (ML) and Artificial Intelligence (AI) experience are required.
- Strong software engineering fundamentals and experience building production systems.
- Experience building Machine Learning (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.
- Tech Stack: Python, PyTorch, JAX, LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM, Cloud infrastructure, Distributed systems, Machine Learning (ML)/data pipelines and workflow orchestration, GPU infrastructure and performance tooling, and Vector databases and retrieval infrastructure.
Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.
Keywords: San Francisco CA Jobs, AI, Artificial Intelligence, Cloud Infrastructure, Data Pipelines, Distributed Systems, GPU Infrastructure, JAX, LLM, Large Language Model, Machine Learning Platform Engineer, ML, Machine Learning, Python, PyTorch, SGLang, TensorRT-LLM, Vector Databases, vLLM, Workflow Orchestration, Work From Home, Remote, California Recruiters, IT Jobs, California Recruiting
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Originally posted on Himalayas