Job Opportunities API

ABOUT OLIX AI is growing faster than any technology in history and the explosion in demand has created a massive infrastructure gap; we can no longer build chips or power stations fast enough to keep up. The industry is still leaning on a ten-year-old hardware blueprint that has reached its limit. A new paradigm that is faster and more efficient will be the biggest economic opportunity of the next century and create the most important company of the next decade. The OLIX Decode Accelerator 1 (DX-1) is the first accelerator architected specifically for decode. Rack-scale co-design of logic, data movement, packaging, optics and interconnect enables a step change in system level performance. THE ROLE We’re searching for an Architect/Staff Systems Software Engineer to own how our next-generation DX-1 accelerator is brought to life as a production inference platform. DX-1 is a dataflow architecture built specifically for decode, deployed in a disaggregated inference environment. Your mission is to make that hardware serve large AI models at rack scale by building and extending the runtime and serving stack that connects PyTorch and JAX down to the metal. This is a whole-stack systems role. You’ll work where the runtime, the network, and the accelerator meet, partnering closely with hardware, compiler, and modelling teams to optimize serving performance. Your impact is measured not only by what you build but by the leverage you create: the standards you set, the systems and tooling other teams build on, and the direction you shape across the platform. RESPONSIBILITIES - Own the Runtime & Serving Stack: Design, build, and extend the distributed inference and serving stack (e.g. vLLM, SGLang, NVIDIA Dynamo, TensorRT-LLM) onto DX-1, rather than treating any layer as a black box. - Scale Distributed Inference: Define how inference scales across many accelerators: tensor / pipeline / data parallelism, collective communication patterns, KV-cache management and offload, and memory-aware scheduling across a disaggregated topology. - Engineer for Reliability at Scale: Make distributed inference dependable across failure domains (fault handling, graceful degradation, load balancing, and recovery), and define the observability, tracing, and tooling standards that let teams diagnose problems across the runtime, network, and accelerator rather than through logs alone. - Drive Bring-Up: Evaluate system behaviour before silicon is fully available (simulation, emulation, FPGA prototyping, analytical modelling), root-cause what breaks during bring-up, and influence design decisions across hardware and software teams. - Set Standards Across Teams: Identify the highest-impact systems problems across teams and make sure they get solved; hold and articulate a clear technical bar and raise peers to it through review, pairing, and direct challenge; build leverage through systems, frameworks, and developing senior talent rather than solving everything personally. - Shape Direction: Bring structure and clear direction to ambiguous, cross-team problems, drive structural improvements with urgency, and shape strategic direction within the platform domain, informed by external research, competitive awareness, and industry connections that help generate talent and partnership pipelines. SKILLS & EXPERIENCE - Deep experience in systems software, with hands-on C/C++ and strong systems fundamentals across the runtime / network / accelerator boundary. - Demonstrated ownership of a hard, end-to-end systems problem, ideally extending a distributed inference / serving stack (vLLM, SGLang, NVIDIA Dynamo, TensorRT-LLM) in production, with specifics on what you built or changed and why. - Distributed inference at scale: parallelism strategies, collective communication, KV-cache and memory management, and reliability across distributed failure domains at cluster scale. - Fluency at the framework boundary, connecting accelerators to PyT