Member of Technical Staff

Grey Machines; India; Remote; INR 4,000,000 - 7,500,000; Full time

Job summary

Grey Machines is building a decision intelligence platform that runs synthetic worlds to test hypotheses and forecast outcomes. As the first senior engineer, you will own the entire engineering stack from architecture decisions to deployment, shaping the platform that powers Monte Carlo simulations, ODE solving, data pipelines and the frontend.

You will work with Python, FastAPI, PostgreSQL and React, and you will build LLM-driven model-building pipelines with robust validators.

Qualifications

• Experience building zero-to-one systems with clear architectural ownership.
• Strong grounding in Bayesian stats and stochastic methods.
• Production-grade Python, FastAPI, PostgreSQL and React development.
• Experience deploying ML/AI models in multi-stage pipelines and LLMs.

Responsibilities

• Make architecture calls and own the consequences.
• Build the core simulation platform including ODE solver and Monte Carlo.
• Lead model-building pipelines with validators and repair loops.
• Ensure data provenance and reproducibility across runs.

Skills

System design

Architecture decisions

Bayesian statistics

Production Python

LLM in production

Education

Maths/CS/Physics/Statistics degree

Tools

Python

FastAPI

PostgreSQL

React

Job description

Grey Machines is a decision intelligence platform that creates synthetic worlds, where decision makers, investors and strategists can test a hypothesis before they commit to it. They ask a \"what if\" question in plain language, such as \"What happens to nickel markets if Indonesia bans ore exports?\", and the platform builds a working model of that world: its actors, feedback loops and delays, grounded in real data and cited evidence. They then watch thousands of possible futures unfold, and see the range of outcomes, what drives them, and a brief in which every number traces back to its source. Getting a big decision wrong in the real world is costly. Testing it in a synthetic world first takes hours. Grey Machines was founded by Sambeet Parija, former Co-Founder and Chief Product Officer of 73 Strings.The platform works end to end and is in private beta. Now it has to be correct, fast and reliable at scale. That is the job.

What You Will Build

You will be the first engineer and the most senior technical person in the room, so the scope is all of engineering. You will make the architecture calls every later engineer builds on, and own the consequences.

• The simulation core: the ODE solver, the Monte Carlo engine, sensitivity analysis, and the convergence and health checks that decide when a result can be trusted
• The model-building pipeline: frontier LLMs build the model in stages, and deterministic validators and repair loops check every step. The real work is deciding what the LLM can be trusted with, what code has to verify, and how to fail safely.
• The data and evidence layer: values from sources such as FRED, the academic literature, the web and the documents users bring, with units, ranges and provenance that survive end to end
• The execution layer: long Monte Carlo jobs are checkpointed and resume after a restart, today on one machine. Next they need to be scheduled and run in parallel across many.
• Production: AWS, deploys, monitoring, tracing and incident response
• The frontend when it needs you: a React 19 app with Canvas 2D and D3 visualisations that make dense quantitative output readable for a strategist, not just a quant
• The engineering bar: testing, review and release discipline, and hiring the team that comes after you

Who You Are

• You have built systems from zero, not just shipped features into existing ones, and lived with your own architecture decisions, including the wrong ones.
• You are fluent in Bayesian probability and statistics, stochastic processes and numerical methods: ODEs, Monte Carlo and convergence diagnostics. A PhD is not required. Being able to reason about what the numbers mean is.
• You write production code fast, debug carefully and care when it is wrong. Production experience with Python, FastAPI, PostgreSQL and React is required: the codebase is built on them.
• You have run LLMs in production: structured output, prompt caching, multi-stage pipelines and the failure modes that only show up there.
• You write clearly. We work remotely and asynchronously, so your design docs and code reviews are how you work.

Strong Signals

None of these is required. They are backgrounds that tend to fit.

• A year or two at a quantitative trading firm (Jane Street, Hudson River Trading, Tower Research, Graviton or similar)
• Your own startup, including one that did not work out. We care about what you learned.
• A maths, CS, physics or statistics degree from a school these firms hire from, such as the IITs, IISc, ISI, CMI, MIT, Stanford, CMU or the Ivy League
• Multi-tenant SaaS or high-throughput compute pipelines
• CTO, Staff or Principal Engineer experience at an early-stage company

What It Is Honestly Like

We are an early-stage startup, and we would rather say so up front.- Priorities move. Something you build this month may be thrown away next month because we learned something.- There is no team behind you yet. You will check your own blind spots and make calls with incomplete information.- Some weeks will feel like a drawdown. We want someone who stays steady through the good weeks and the bad ones, and treats uncertainty as part of the job.In return you get real ownership, founder-level say in the product and the company, and a genuinely hard applied-maths problem. Fully remote, flexible hours.