AI / ML Solutions Engineer

About Anyscale

At Anyscale https://www.anyscale.com/, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray https://docs.ray.io/en/latest/, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt/, Uber https://www.uber.com/blog/horovod-ray/, Spotify https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/, Instacart https://www.youtube.com/watch?v=3t26ucTy0Rs&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=23&pp=iAQB, Cruise https://www.youtube.com/watch?v=gj0BqvfX_wI&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=46&pp=iAQB, 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 https://www.wsj.com/articles/ai-startup-anyscale-adds-99-million-to-andressen-horowitz-led-funding-round-11661254200 with $250+ million raised to date.

About the role

We 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 DO

Customer Delivery & Implementation

- Implement production AI / ML workloads using Ray and Anyscale, such as:

- Distributed model training

- Scalable inference and serving

- Data preprocessing and feature pipelines

- Work hands-on with customer codebases to refactor or adapt existing workloads to Ray

Architecture & Technical Guidance

- Advise customers on ML system architecture, including:

- Application design for distributed execution

- Resource management and scaling strategies

- Reliability, fault tolerance, and performance tuning

- Guide customers through architectural and operational changes required to adopt Ray and Anyscale effectively

MLOps Enablement

- Partner with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows

- Support CI/CD, monitoring, retraining, and operational best practices

- Help customers transition from experimentation to production-grade ML systems

Technical Enablement & Knowledge Transfer

- Enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance

- Contribute feedback from the field to product, engineering, and education teams

- Help develop reference architectures, examples, and best practices based on real customer use cases

WHAT WE’RE LOOKING FOR

Required Experience

- 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or ML Systems Engineer

- Strong proficiency in Python and experience building production ML systems

- Hands-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 inference

- Data pipelines and workflow orchestration

- Comfort working directly with customers in a consultative, problem-solving role

- Strong communication skills and ability to explain technical tradeoffs clearly

Preferred Experience

- Experience supporting or deploying ML platforms or internal ML infrastructure

- Familiarity with cloud environments (AWS, GCP, Azure)

- Exposure to MLOps tooling (MLflow, Airflow, Dagster, Kubeflow, etc.)

- Prior experience in Professional Services, Consulting, or Customer Engineering roles

WHY JOIN ANYSCALE PROFESSIONAL SERVICES

- Work directly on real-world AI / ML systems at scale

- Partner with leading ML teams across industries

- Influence how Ray and Anyscale are adopted in production environments

- Combine deep engineering work with customer impact

- Competitive compensation, equity, and flexible remote work

Anyscale 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 federal or state law. 

Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1Kt2S6_k_SjxaEdGowH4rngVdg2ApAQV3/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1K3Nz72xgsU2hngnVUEu53wEeZjbAMbnZ/view?usp=sharing