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At the heart of orchestrating this monumental compute infrastructure is the Compute team. This team is dedicated to building and maintaining the foundational technology that solves the fundamental challenges of resource scheduling, task isolation, and distributed state consistency across our massive batch compute fleet. At our scale, traditional off-the-shelf orchestrators break. The Compute team builds the custom engine - BatchAPI - that manages the lifecycle of millions of tasks - built on top of K8s primitives but implements our own custom scheduler. We deal with the 'unsolved' problems of distributed computing: maximizing hardware utilization while ensuring that a failure in one node doesn't cascade across the entire cluster. This engine is engineered to handle massive scale, ensuring reliability, efficiency, and rapid turnaround for our engineers.
Job Responsibility:
Design, implement, and maintain core components of the high-performance, large-scale distributed batch compute engine (BatchAPI). Architect and optimize the scheduler, resource allocator, and execution engine of BatchAPI to handle bursty, heterogeneous workloads with minimal overhead
Design low-latency APIs and resilient communication protocols that bridge our Python SDK with the Golang-based core engine
Develop high-level workflow abstractions, enabling engineers across the company to programmatically define, deploy, and manage complex data processing, simulation, and ML training pipelines
Solve complex problems in distributed locking, throttling, and fair-share scheduling to ensure multi-tenant stability
Drive continuous improvements in the performance, scalability, and resilience of the entire compute infrastructure, implementing robust monitoring and alerting systems to maintain operational excellence for critical workflows
Collaborate closely with infrastructure and product engineering teams (e.g., Autonomy, Data, Simulation, Machine Learning) to gather requirements, provide expert consultation, and integrate compute workflows with key company systems
Requirements:
5+ years of professional software engineering experience
Deep expertise in Golang (for core systems) and Python (for SDK/API layering)
Strong understanding of distributed systems fundamentals (e.g., CAP theorem, consensus algorithms, or gossip protocols)
Experience with performance profiling and tuning (e.g., memory management, I/O bottlenecks, or network latency optimization)
Specialized knowledge of container orchestration systems like Kubernetes
Proven track record of driving continuous performance, scalability, and resilience improvements in production environments managing critical data
Familiarity with cloud provider compute and data services (e.g., AWS EKS, S3, RDS)
Nice to have:
Experience working with computational workloads specific to the autonomous vehicle, robotics, or large-scale machine learning domains (e.g., data processing for perception, simulation, or model training)
Demonstrated ability in creating and refining user-facing tools, including adeptness at incorporating user feedback, managing expectations, and effectively prioritizing development based on user needs