Job Description

ppbAbout Anyscale: /b /p pAt Anyscale, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI, Uber, Spotify, Instacart, Cruise, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. /p pWith Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. /p pProud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. /p pbAnyscale /b is looking for a bSr/Staff AI Engineer /bto be a btechnical advocate /b for bRay /b within the bML and AI community /b. You’ll spend your time building real AI systems with Ray, writing code and demos, and sharing your experience through blogs, talks, videos, and open technical discussions. You will be responsible for driving Ray adoption. The role focuses on educating, inspiring, and motivating engineers on the value of Ray to power their AI workloads. The role centers on technical content and evangelism that demonstrates an understanding of the requirements for AI workloads, the challenges and implications of not using Ray, and the technical value and differentiation of Ray. /p pThis is bNOT /b a marketing role. We're looking for someone who has built ML systems in production, understands the pain of distributed training and inference at scale, and can credibly teach others how to solve these problems with Ray. /p pYou’ll work at the intersection of distributed systems and modern AI, from scaling LLM training and fine-tuning, to building production RAG pipelines, to orchestrating agentic AI systems. /p h3bWhat You'll Do /b /h3 ul lipbEngage the Community: /b Present at conferences, participate in the open-source community, speak at first-party and third-party in-person and virtual events, build relationships with relevant community organizers in region, and engage with ML practitioners on GitHub, Discord, and social platforms /p /li lipbLearn, Build Share: /b Create production-quality demos, sample applications, and reference architectures that showcase Ray's capabilities across different AI workloads. /p /li lipbBe a Subject Matter Expert: /bDevelop deep expertise in one or more of Ray's core workload areas, distributed training, LLM serving, and agentic AI, becoming a trusted technical authority both internally and in the broader ML community. /p /li lipbTeach Educate: /b Develop technical content (blogs, tutorials, workshops, videos) that helps ML engineers understand how to scale their workloads /p /li lipbShape the Product: /b Bring real-world feedback from the community back to engineering and product teams; contribute to Ray's open-source libraries where appropriate /p /li lipbResearch Experiment: /b Stay current with ML/AI research and translate emerging techniques into practical, scalable implementations on Ray /p /li /ul pYou're an bML Engineer or AI Researcher /b who: /p ul lipLives in a major AI hub in EMEA (like London) /p /li lipHas 4+ years of hands-on experience building ML/AI systems (training, fine-tuning, inference, RAG, agents) /p /li lipHas practical experience building end-to-end ML pipelines or deploying models to production using ML platforms (e.g., OSS Ray, Amazon SageMaker, Vertex AI, Azure ML, Databricks, or similar) /p /li lipHas some experience with technical writing, teaching, conference speaking, or open-source contributions /p /li lipCan write production-quality Python code and work fluently with PyTorch, HuggingFace, or similar frameworks /p /li lipIs genuinely excited about helping others learn and succeed /p /li lipEnjoys traveling and speaking publicly /p /li lipMay not have formal DevRel experience, but has demonstrated teaching/sharing through bat least one /b of the following: /p /li /ul ul lipOpen-source contributions with good documentation /p /li lipTechnical blog posts or tutorials /p /li lipConference talks, meetup presentations, or workshop facilitation /p /li lipResearch papers or technical reports /p /li lipActive engagement in ML communities (GitHub, Discord, Reddit, Twitter/X) /p /li /ul h3bPreferred qualifications: /b /h3 ul lipStrong Python programming and software engineering fundamentals /p /li lipDeep hands-on experience with bat least one /b ML framework (PyTorch, TensorFlow, JAX, scikit-learn) /p /li lipSolid understanding of ML fundamentals: model architectures, training loops, loss functions, optimization, evaluation metrics, etc. /p /li lipExperience with the ML development lifecycle: data preprocessing, feature engineering, model training, hyperparameter tuning, model evaluation /p /li lipFamiliarity with LLM concepts: fine-tuning (LoRA, QLoRA, full fine-tuning), RLHF, tokenization, MoE, etc. /p /li /ul h3bNice to Have: /b /h3 ul lipUnderstanding of distributed systems concepts (parallelism, fault tolerance, resource management) /p /li lipPrior experience with Ray or similar distributed computing frameworks /p /li lipExperience with agentic AI systems and multi-agent orchestration /p /li lipGPU programming knowledge (CUDA, optimization techniques) /p /li lipUnderstanding of inference optimization: quantization, batching, KV caching, speculative decoding /p /li lipPublished research or significant open-source contributions /p /li lipExisting presence in the ML/AI community (research or industry) /p /li lipExperience with cloud platforms (AWS, GCP, or Azure) /p /li /ul h3Location Eligibility /h3 ul lipThis role is bbased in London, UK /b, with a hybrid work arrangement /p /li lipCandidates must be beligible to work in the UK /b /p /li lipRegular travel across EMEA is expected /p /li /ul h3Compensation /h3 pAt Anyscale, we take a market-based approach to compensation. We are data-driven, transparent, and consistent. As market data evolves, the target salary range for this role may be adjusted accordingly. /p pAnyscale Inc. is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by law. /p /p #J-18808-Ljbffr