AI / ML Solutions Engineer

About AnyscaleAt 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.With 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.Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date.About the roleWe are looking for an AI / ML Solutions Engineer to join Anyscale’s Professional Services team and work directly with customers to design, implement, and scale machine learning and AI workloads using Ray and Anyscale.This role is ideal for a hands-on Machine Learning Engineer or MLOps Engineer who enjoys solving real-world production problems alongside customer teams. You will guide customers through architectural decisions, application refactors, and operational best practices as they adopt Ray for distributed training, data processing, inference, and ML workflows.In addition to implementation work, you’ll play a key role in enabling customer ML and MLOps teams—helping them understand why architectural changes are needed and how to successfully operate Ray-based systems in production.What You’ll DoCustomer Delivery & ImplementationImplement production AI / ML workloads using Ray and Anyscale, such as:Distributed model trainingScalable inference and servingData preprocessing and feature pipelinesWork hands-on with customer codebases to refactor or adapt existing workloads to RayArchitecture & Technical GuidanceAdvise customers on ML system architecture, including:Application design for distributed executionResource management and scaling strategiesReliability, fault tolerance, and performance tuningGuide customers through architectural and operational changes required to adopt Ray and Anyscale effectivelyMLOps EnablementPartner with customer MLE and MLOps teams to integrate Ray into existing platforms and workflowsSupport CI/CD, monitoring, retraining, and operational best practicesHelp customers transition from experimentation to production-grade ML systemsTechnical Enablement & Knowledge TransferEnable customer teams through working sessions, design reviews, training delivery, and hands-on guidanceContribute feedback from the field to product, engineering, and education teamsHelp develop reference architectures, examples, and best practices based on real customer use casesWhat We’re Looking ForRequired Experience5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or ML Systems EngineerStrong proficiency in Python and experience building production ML systemsHands-on experience with distributed systems or scalable ML frameworks (Ray, Spark, Dask, Kubernetes, etc.)Experience with one or more of:Distributed training (multi-node / multi-GPU)Model serving and scalable inferenceData pipelines and workflow orchestrationComfort working directly with customers in a consultative, problem-solving roleStrong communication skills and ability to explain technical tradeoffs clearlyPreferred ExperienceExperience supporting or deploying ML platforms or internal ML infrastructureFamiliarity with cloud environments (AWS, GCP, Azure)Exposure to MLOps tooling (MLflow, Airflow, Dagster, Kubeflow, etc.)Prior experience in Professional Services, Consulting, or Customer Engineering rolesWhy Join Anyscale Professional ServicesWork directly on real-world AI / ML systems at scalePartner with leading ML teams across industriesInfluence how Ray and Anyscale are adopted in production environmentsCombine deep engineering work with customer impactCompetitive compensation, equity, and ... (truncated, view full listing at source)