Full Stack Software Engineer

• Compensation: ₹15L – ₹50L
• 0.1% – 1.0%

We're an early-stage team building a 0-to-1 product for the next-gen power systems.

You'll be part of small top-notch team. That means working with high ownership and autonomy in an intense startup environment.

What we're looking for

2+ years building full stack products.

Strong React.

Comfortable with complex client-side state, performance work, and interfaces that go well beyond CRUD forms.

Strong Python

FastAPI or an equivalent async framework. You know how to design an API that ages well.

Real rigour.

You write tests, review carefully, and think about edge cases before production finds them for you.

Engineering discipline.

Version control hygiene, CI, code review, documentation, observability. Best practices as habit.

Ownership instinct.

You chase down ambiguity instead of waiting for a spec.

Nice to have

• Distributed job execution and orchestration — Celery, Ray, Dask, Kubernetes, or equivalents
• Scientific or numerical computing at scale (NumPy, SciPy, pandas)
• Rich interactive UI work — canvas rendering, graph editing, custom visualization (React Flow, D3, WebGL)
• Building on LLM APIs beyond prototypes: evals, structured outputs, retrieval, cost and latency control
• Any background in engineering, physics, or optimization

How we work

We move fast at high intensity. Short cycles (a micro-standup where the team catches up every ~2.5 hours), direct feedback, and a high bar for quality.

Impact

• Build tools that help the world navigate the energy transition intelligently
• Direct influence on architecture and product direction
• A genuinely hard technical problem in an impactful space

• 2+ years building full stack products
• Strong React, including complex client-side state and performance work
• Strong Python with FastAPI or an equivalent async framework; API design skills
• Real rigour: writes tests, reviews carefully, considers edge cases
• Engineering discipline: version control hygiene, CI, code review, documentation, observability
• Ownership instinct — chases down ambiguity without waiting for a spec
• Distributed job execution and orchestration (Celery, Ray, Dask, Kubernetes or equivalents)
• Scientific or numerical computing at scale (NumPy, SciPy, pandas)
• Rich interactive UI work (canvas rendering, graph editing, React Flow, D3, WebGL)
• Building on LLM APIs beyond prototypes (evals, structured outputs, retrieval, cost/latency control)
• Background in engineering, physics, or optimization