About Anyscale:

At 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.

Anyscale is based in San Francisco, CA. Employees are required to come in office 3x a week.

Anyscale is actively seeking talented engineers to join our team and contribute to the development of next-generation, high-performance machine learning serving systems. We value diversity and inclusion, and we encourage individuals from underrepresented groups to apply.

Many existing ML serving tools are inherited from previous infrastructure generations, but emerging ML applications present new requirements, such as high compute demands, specialized hardware needs, and the integration of multiple models and business logic within a single request. At Anyscale, our mission is to provide a powerful yet simple set of tools that enable the seamless deployment of complex ML applications in production.

About the Platform Team:

The Platform team is dedicated to creating world-class systems for serving ML models in production. This includes building and maintaining the open-source Ray Serve library and contributing directly to the Anyscale platform used by our customers for critical applications. Our work is often user-facing, allowing you to collaborate with open-source users and customers ranging from lean ML engineering teams at small startups to industry-leading companies such as Uber, Shopify, and ByteDance.

As part of this role, you will:

Develop a highly available service for ML model serving.
Enhance Ray Serve and our other libraries to simplify the development of next-generation ML applications in production.
Improve our autoscaling capabilities to drive performance enhancements and cost savings.
Optimize latency and throughput for both single- and multi-model serving scenarios.

We’d love to hear from you if you have:

A solid background in algorithms, data structures, and system design.
Experience working with modern machine learning tooling, including PyTorch, TensorFlow, and JAX.
At least 2+ year of relevant work experience.

Bonus points!

Experience in building and maintaining open-source projects.
Experience in building and operating machine learning infrastructure in production.
Experience in building highly available serving systems.

Compensation

At Anyscale, we take a market-based approach to compensation. We are data-driven, transparent, and consistent. The target salary for this role is $170,112 ~ $237,000. As the market data changes over time, the target salary for this role may be adjusted.

This role is also eligible to participate in Anyscale’s Equity and Benefits offerings, including the following:

Stock Options
Healthcare plans, with premiums covered by Anyscale at 99%
401k Retirement Plan
Wellness stipend
Education stipend
Paid Parental Leave
Flexible Time Off
Commute reimbursement
100% of in office meals covered

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 and the Right to Work posters in English and Spanish

Compensation Overview

As part of this role, you will:

We’d love to hear from you if you have:

Bonus points!

Compensation

6 month growth

1 year growth

2 year growth

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