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Open nowPosted 73 days ago

Senior Data Scientist ( Kernel Optimisation & Inference Engineer)

Eka.care26 open roles

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Bengaluru
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Your applicationOpen nowSenior Data Scientist ( Kernel Optimisation & Inference Engineer)Eka.care · Bengaluru

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This job: posted 73 days ago

The posting

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
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