Distributed Systems Engineer – Vector Database

hace 1 semana

Municipio de Rosario, Argentina HI Technology & Innovation A tiempo completo

About the Role HI Technology & Innovation is collaborating with a pioneering database company to evolve a single-threaded, command-line vector database proof-of-concept, originally written in C#, into a demonstrable, parallelized prototype. We’re looking for an experienced Distributed or Parallel Processing Engineer to lead the development of a lightweight “controller + wrapper” layer that adds multi-instance orchestration, parallelism, and query merging capabilities to the current system. This is a high-impact, fast-paced role ideal for someone with strong distributed systems thinking and a pragmatic, get-it-done engineering mindset. What You’ll Tackle Design and implement a wrapper / controller layer around the existing single-threaded vector DB POC. Enable sharding and parallel loading of large vector datasets across multiple instances. Implement logic to dispatch and coordinate parallel queries, aggregate candidate vectors, apply a pluggable distance metric (e.g., Euclidean, cosine similarity, SAD / Manhattan), and merge and sort results before returning to the caller. Expose both command-line and REST interfaces to interact with the system. Output clear timing metrics to demonstrate speed-ups from parallel execution. Deliver a working, testable prototype — production-ready polish is not required. What We’re Looking For Deep expertise in distributed computing and parallel processing, including MapReduce-style design patterns, data sharding, and inter-process communication (IPC). Proven track record as a senior engineer who can work independently, move quickly, and deliver results with minimal oversight. Strong problem-solving skills and practical experience building distributed systems and prototypes. Comfort working across multiple languages and runtimes. Choose whatever tech stack gets the job done fastest (Python, C#, C, etc.). Ability to clearly demonstrate and benchmark performance improvements. Engagement Snapshot Timeline: Short-term, estimated 5–10 days of focused work. Start Date: ASAP Location: Remote Deliverables: Fully functional parallel prototype with CLI + REST interfaces, benchmark metrics, and sharded query support. Ready to build something fast and clever? Apply now and help bring this proof-of-concept to life. #J-18808-Ljbffr

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