Senior Data Scientist ( Kernel Optimisation & Inference Engineer)

Location: Bengaluru, India

Department: Data Science.

Experience: 2 - 5

• 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.

• 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.

• 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.

• 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.

• Medical Insurance & Accidental Insurance
• Maternity & Paternity Benefits
• PF, Gratuity, & Leave Encashment
• Salary Advance Policy