Software Engineer (Ray Data)

About the role

Ray Data scales its engine to manage diverse production demands for reliable AI workloads. You will own the design and implementation of core components within the Ray Data engine that power data ingestion, transformation, and serving for AI applications. This role requires you to analyze and optimize database and processing internals to ensure the engine behaves efficiently under diverse production loads. You will be responsible for removing performance bottlenecks that limit AI application success through deep systems expertise and rigorous tuning. Collaboration with data scientists, data engineers, and product teams is essential to translate requirements into scalable data processing solutions. You will contribute to a critical open-source project that serves as the foundation for distributed AI workloads across the industry. Your work will directly impact the reliability and throughput of production AI pipelines for customers relying on Anyscale.

Key facts

What you'll do

Analyze the internals of database and processing systems to optimize engine behavior and data flows for production AI workloads. Build scalable, fault-tolerant distributed systems that form the backbone of the Ray Data engine handling diverse production demands. Diagnose and remove performance bottlenecks that limit AI application success through deep systems expertise and targeted tuning. Study data flows and engine behavior to implement optimizations that improve reliability and throughput in production environments. Partner with data scientists and data engineers to understand complex requirements and translate them into robust data processing solutions. Instrument and observe system behavior to identify issues and validate improvements in real-world deployment scenarios. Contribute to an open-source project that enables distributed computing for software developers of all skill levels. Drive initiatives to enhance data ingestion, transformation, and serving capabilities within the Ray ecosystem. Collaborate closely with cross-functional teams to align technical solutions with product goals and customer needs. Mentor and guide junior engineers by sharing best practices in distributed systems and data engineering.

Requirements

Relevant work experience of at least 3 to 4 years is required to handle distributed systems challenges in AI workloads. Handling these distributed systems challenges in AI workloads requires building scalable, fault-tolerant distributed systems that meet production standards. Database and processing internals are studied to optimize engine behavior and data flows for demanding use cases. Studying these internals ensures engine behavior and data flows are optimized for production stability and performance. Performance bottlenecks limiting AI application success are removed through deep systems expertise, tuning, and iterative improvement. Removing these limitations requires deep systems expertise, strong coding skills, and disciplined debugging in production environments. You must demonstrate the ability to read, write, and maintain Python code that interfaces with Ray internals and data processing frameworks. Proven experience with distributed computing concepts such as scheduling, fault tolerance, and state management is essential for success in this role. A Bachelor's degree is required as a baseline qualification for understanding complex data systems and engineering principles.

Nice to have

Experience contributing to open-source projects related to distributed systems or data processing frameworks. Familiarity with cloud platforms and infrastructure for deploying and operating distributed services. Knowledge of data serialization formats, storage systems, and networking protocols that underpin modern data platforms.

Practical notes

Some companies give a take-home analysis. Typical interview steps include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

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.