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Software Engineer (Ray Core)

Optimizing performance of large-scale workloads on Ray
San Francisco, California, United States
$170,112 - $237,000 USD per year
5 months ago
Anyscale

Anyscale

Ray is the most popular open source framework for scaling and productionizing AI workloads.

✨ About The Role

Software Engineer (Ray Core)

San Francisco, CA
Engineering – Runtime /
Full-time
/ On-site
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.

About the role:
Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend.

About the Runtime team :
The Runtime team develops and maintains the Ray C++ backend (e.g., distributed scheduler, language runtime integration, I/O and memory subsystems). We are responsible for the reliability, scalability, and performance of Ray as well as ensuring that Ray provides the right feature set to support higher level libraries and use cases. The team works on a balance of new features / distributed libraries, test infra improvements, debugging, and longer-term architectural improvements to Ray.

A snapshot of projects you can work on:
- Optimizing performance of large-scale workloads on Ray
- Stability and stress testing infrastructure
- Improving fault tolerance (HA)

As part of this role, you will:

    • Develop high quality open source software to simplify distributed programming (Ray)
    • Identify, implement, and evaluate architectural improvements to Ray core
    • Improve the testing process for Ray to make releases as smooth as possible
    • Communicate your work to a broader audience through talks, tutorials, and blog posts

We'd love to hear from you if have:

    • At least 2 year of relevant work experience
    • Solid background in algorithms, data structures, system design
    • Experience in building scalable and fault-tolerant distributed 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
    • Paid 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
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Software Engineer (Ray Core)
San Francisco, California, United States
$170,112 - $237,000 USD per year
Software
About Anyscale
Ray is the most popular open source framework for scaling and productionizing AI workloads.