Pear VC

AI Data Engineer - Optexity (India)

Pear VC Bengaluru, Karnataka, India

Venture Capital and Private Equity Principals · 11-50 employees

20 h ago
data-engineer Mid (2-5 yrs) Full-time India
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About the role

You will work with proprietary clinical data to build and evaluate reasoning models that improve physician workflows. You will also own the end-to-end training and evaluation pipeline while publishing technical reports on model performance.

What they look for

Python Machine Learning LLM Data Engineering Clinical Reasoning Model Training Model Evaluation Research Data Analysis Computer Use Models Healthcare Data Technical Writing Benchmarking Pipeline Development

Requirements

The ideal candidate has professional research experience and is fluent in Python with a background in training models from scratch. A bachelor's degree in computer science or a related field is required, along with a strong desire to work in a fast-paced startup environment.

Benefits

Competitive salary Meaningful equity

Full description

About Optexity

Optexity is a product-driven research lab building clinical reasoning and computer use models on data no other lab can reach. We deploy directly inside clinics and hospitals — including systems with no APIs, using integration infrastructure we've built and open-sourced — and in return become their preferred partner. That gives us proprietary clinical reasoning trajectories from practicing physicians, and a feedback loop between real patient encounters, our models, and the products built on top of them.

We're a small, fast-moving founding team with multiple published papers in NeurIPS, ICML, CVPR etc and background from Apple, Amazon, Microsoft, CMU, IIT.

We are backed by world-class investors and leaders like Jeff Dean, Neotribe VC, PearVC, Together Fund and Zapier Fund.

 

How we work

  • Customer obsession — we start with the customer and work backwards
  • Intellectual honesty — ideas matter more than titles; we communicate directly and assume good intent, even in disagreement
  • Bias for action — we build and learn with customers rather than debate in the abstract
  • Extreme ownership — we own outcomes, not just tasks, and see problems through

Why this role exists

Most research roles at this stage hand you a dataset everyone already has and ask you to be marginally better than the last person who tried. Here you get data nobody else has — real clinical reasoning trajectories from practicing physicians — and the room to figure out what to do with it. This is a founding research hire: you'll define the agenda as much as execute it, with direct founder access, real compute, and nothing between an idea and an experiment.

What you'll do

  • Work with real, proprietary clinical data from hospital and clinic partners to surface insights that shape model and product direction
  • Build clinical reasoning models that improve physician and clinic workflows
  • Design and publish benchmarks that expose where current LLMs fall short on real clinical reasoning
  • Evaluate computer-use models on real-world tasks
  • Own the training and evaluation pipeline end to end
  • Take open-ended problems from question to working model, with minimal predefined structure
  • Write up findings as technical reports and papers

Ideal candidate

  • Has real research experience — can define a problem, not just execute a known one
  • Has trained models before (not just fine-tuned APIs) and is fluent in Python
  • Bachelor's in computer science and related fields.
  • Energized by open-ended problems and ambiguity
  • Wants to publish, not just ship
  • Takes ownership without needing to be guided
  • Genuinely wants a startup over big tech — speed and ambiguity as defaults, not exceptions

Nice to have

  • Prior founder or founding-engineer experience
  • Strong product taste
  • Worked on healthcare datasets before
  • Built LLM-powered products before
  • Ambitions to start your own company someday — we'll support that path

What you get

  • Direct, daily work with the founders, plus exposure to board and investor conversations
  • Real ownership of technical direction, with scope that grows as the company does
  • Full compute and data to do the work properly
  • Competitive salary and meaningful equity

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