Kernel Optimisation & Inference Engineer Bengaluru · Full-time · 2–5 yrs Somewhere between the model and the silicon, 10–20% of a training budget goes missing. Your job is to go get it back, and then make the same model fast enough to run in a clinic, and small enough to run on a phone. About EkaCare and the mission EkaCare is India's connected healthcare platform: an EMR that doctors run their practices on, a personal health record used by millions of Indians, and one of the deepest integrations with India's ABDM digital-health rails. Our Parrotlet family of medical models already serves Indian doctors in production, and we open-source our work where it counts. The role: You'll work with our performance lead on making everything fast: training-side fused kernels and MFU on the 30B MoE, inference-side latency and throughput, and the quantised 2B/4B on-device tier. Hardware-up: profiler first, roofline reasoning always, custom kernels when the math says so. What you'll do • Profile training and inference workloads and hunt utilisation gaps across kernels, memory and comms. • Write and tune CUDA kernels where existing ops leave real performance on the table, and know when they don't. • Optimise MoE-specific paths: grouped GEMMs, all-to-all communication, expert load imbalance. • Build the fast inference path: vLLM-class serving, continuous batching, prompt/prefix caching for clinical-context workloads, speculative decoding. • Own quantisation for the 2B/4B variants (AWQ/GPTQ-class, fp8) — with eval-parity verification, not just perplexity. • Make on-device inference real for the hardware Indian clinics actually have. What we look for • 2–5 years in GPU performance work; you've profiled real workloads and shipped optimisations with before/after numbers you can defend. • Working fluency in CUDA, and memory-hierarchy reasoning (coalescing, occupancy, SRAM tiling; you can explain *why* FlashAttention is fast). • Hands-on with a modern serving stack (vLLM, TensorRT-LLM, SGLang or similar) beyond just running it. • Measurement discipline: you profile before optimising and verify correctness after. Bonus • fp8 experience on H100/H200-class hardware; torch.compile/inductor internals. • Quantisation research or on-device/mobile inference experience. • Open-source kernels or serving contributions. Why this is a rare gig • Open source, with your name on it: weights and technical reports ship publicly. • India-scale mission: models for a billion people in their own languages. • Compute that’s rare to fine: dedicated multi-node H200 training under a national grant. • Small senior team: you work with the people who own the recipe. • A live deployment path: Government institutes, EkaCare's doctors and patients use what you ship. Full-Time Employee Benefits • Medical Insurance & Accidental Insurance • Maternity & Paternity Benefits • PF, Gratuity, & Leave Encashment • Salary Advance Policy