Engineering Manager, Observability (TLM)

About the Role

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. We are seeking a Manager to join our team focused on building user-facing application features for the Anyscale AI platform. The role involves interacting with users, understanding their requirements, designing and implementing features, and finally maintaining and improving these features over time. The backend of the platform generally deals with implementing the core business logic of these features. The Workspace & Observability Team is dedicated to empowering clients to create robust AI applications using our powerful platform built on Ray. We are a collaborative group of experts committed to providing bespoke monitoring tools and integrations that enhance the development lifecycle. In particular, these tools accelerate the process of writing, debugging, deployment, and monitoring of AI applications. Observability in a distributed cluster can deal with a ton of data. There are a ton of interesting problems to solve around how to ingest, aggregate, format, and ultimately present that data to our users in a digestible way. With Ray and Anyscale, we have the opportunity to provide great tools out of the box for our users. Join us in shaping the future of AI application development!

Responsibilities

• The Ray Dashboard observability tool which gives users insight into their Ray application including what code is running in which machine, how much data is being moved between various machines, and the hardware utilization of each machine.
• Library-specific observability tools like the Ray Train dashboard or Ray Serve dashboard which accelerates our users ability to develop distributed training or model serving applications.
• Unified log viewer, a tool that ingests logs across a ray cluster and presents the ability to query those logs in meaningful ways, such as by function name, log level, timestamp, or machine.
• Anomaly detection. The ability for the Anyscale platform to automatically detect performance bottlenecks or bugs in our users workloads and suggest or automatically fix these issues.
• Work with a team of leading distributed systems and machine learning experts.
• Communicate your work to a broader audience through talks, tutorials, and blog posts.
• Help us to build and shape a world class company.

Requirements

• Proficiency in backend or full stack development, including experience with web API frameworks and databases.
• Proficiency in Python or an ability to quickly learn new programming languages.
• Good understanding of AI and machine learning concepts.
• Experience with observability tools and monitoring solutions (e.g., Datadog, Splunk, AWS CloudWatch).
• Familiarity with Ray or similar distributed systems frameworks.
• Solid background in debugging, architecture design, and coding.
• Excellent problem-solving skills and a collaborative mindset.
• Passion for building tools that enhance user experience and optimize workflows.

Qualifications

None explicitly listed as "preferred" or "nice-to-have".