Software Engineer, Machine Learning Services (MLS)

• Design, build, and operate the core MLS platform. This includes our Rust-based API gateway, Python ML compute workers, and the distributed job queue that orchestrates it all.
• Solve hard concurrency, performance, and distributed systems problems to ensure our platform is bulletproof for high-volume production workloads.
• Work directly with product and ML science teams to understand their needs and build the scalable infrastructure required to bring their models to life-from massive GenAI models to fine-tuned, specialized classifiers.
• Develop our custom-built, content-addressable storage abstraction layer over cloud object stores (GCS, S3, Azure Blob), complete with its own garbage collection and sharding logic.
• Enhance our asynchronous job-queueing system, built from the ground up on the storage layer using compare-and-swap primitives for atomicity. No off-the-shelf message broker could handle our specific needs.
• Dive deep into the entire stack, from Kubernetes and container orchestration, through gRPC-based service communication, to the performance tuning of ONNX-based inference on GPU-accelerated hardware.
• Write clean, efficient, and rigorously tested code. We value simplicity, correctness, and peer review.

• A solid track record (5+ years) of engineering and architecting large-scale, distributed commercial services. Your experience speaks for itself.
• Deep proficiency in a systems-level language (Rust, C++, Go). A willingness and curiosity to become an expert in Rust is essential, as it's the foundation of our core services. Strong Python skills are also critical.
• Real-world experience with cloud ecosystems (Azure, AWS, or GCP) and containerization (Docker, Kubernetes). You should understand how production systems are deployed, monitored, and scaled.
• A firm grasp of concurrency, multithreading, and asynchronous programming. You know the difference between a mutex and a channel, and you know when (and when not) to use them.
• A pragmatic understanding of computer science fundamentals. We care more about your ability to solve real-world problems with data structures and algorithms than your ability to recite them from a textbook.
• An opinion on what makes good code and good architecture, and the ability to articulate it. You should be comfortable challenging assumptions (including our own) and contributing to a culture of continuous improvement.
• You're a builder and a problem-solver at heart.

Nice to Haves (but we can teach you):

• You've already worked with Rust in a production environment.
• Experience with MLOps, particularly the challenges of managing the lifecycle of models in a multi-tenant, high-availability system.
• Familiarity with building ML inference services, model serialization (e.g., ONNX), and GPU programming (CUDA).
• You've built or worked on custom storage or job-queueing systems before and have the scars to prove it.

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