Senior Data Scientist ( Kernel Optimisation & Inference Engineer)

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