---
title: "Founding Researcher & EngineerFounding Researcher & Engineer"
description: "This role owns research and engineering across our models: training them, serving them, and building products on top of them."
canonical: "https://www.fermionresearch.com/careers/founding-researcher-engineer/"
source: "Fermion Research"
---

## [Careers](https://www.fermionresearch.com/careers/)

# Founding Researcher & Engineer

**Fermion Research** · Remote · Full-time

We compress intelligence into a milliwatt.

[Fermion](https://www.fermionresearch.com/) trains models natively at extremely low precision, writes its own kernels for each chip, and designs its own weight format. Our ternary format stores each weight in 1.58 bits.

- **[Neutrino-1](https://www.fermionresearch.com/research/neutrino-8b/):** an 8B language model in a 2.56 GB download. It outscores Llama-3.1-8B and Gemma-4 on knowledge benchmarks, models that need around 16 GB. ([launch](https://x.com/yoitsmanan/status/2081793414472008008), [weights](https://huggingface.co/FermionResearch))
- **[Phonon-2](https://www.fermionresearch.com/research/phonon-2/):** a 164 MB speech model that is more accurate than Whisper large and transcribes an hour of audio in 6 seconds on a MacBook Air. It has been the #1 trending speech recognition model on [Hugging Face](https://huggingface.co/FermionResearch/Phonon-2) since launch. ([launch](https://x.com/yoitsmanan/status/2104990913886031993))
- **[Our runtime](https://pypi.org/project/fermion-research/):** both models run in it on Mac, Linux, Windows and NVIDIA GPUs.
- **[Detta](https://www.fermionresearch.com/products/detta/):** our Mac dictation app, built on Phonon-2.

This role owns research and engineering across our models: training them, serving them, and building products on top of them.

## Research and training

- Push low-bit training further: new architectures, formats below 1.58 bits, and keeping accuracy as precision drops.
- Train and adapt language, speech and decision models, using pretraining, distillation, preference and RL tuning, and long context.
- Build the data pipelines: generation, filtering, decontamination and labelling.
- Build the in-house evaluations and hold every release to them.
- Run large training jobs on GPU clusters reliably and at sensible cost.

## Serving and deployment

- Run our models behind our API: inference servers, latency, throughput, autoscaling and GPU cost.
- Lead kernel work for each target: CUDA, Metal and MLX, Core ML and the Neural Engine, and CPU.
- Deploy custom models for enterprise customers on their cloud, their own servers or their devices, trained on their data.

## Products

- Work with the product team to build Detta and new consumer apps around our models.
- Turn customer problems into models that ship.

## About you

- You have run inference in production: batching, caching, CUDA graphs, autoscaling, and the cost per million tokens that comes out of it.
- You have worked deep in low-bit: ternary or sub-4-bit weights, quantization-aware training, lookup-table formats, or architectures built around them.
- You have done serious post-training: SFT, distillation, preference and RL tuning, and you know how to keep a model's capabilities intact through it.
- You have shipped quantization that holds up, from GPTQ and AWQ-style post-training methods to QAT and mixed precision, and you know where each one breaks.
- You think in rooflines: you know whether a kernel is bound by memory bandwidth or compute on a given chip, what layout and fusion it needs, and how to get it built.
- You build evals you would bet a release on: decontaminated, paired, with error bars, and hard to game.
- You are comfortable working directly with the product team and with customers.

Bonus: you have trained or deployed speech recognition models (CTC, RNN-T or TDT), or decision models that return calibrated choices, scores and classifications instead of free text. Experience with distributed training at scale also helps.

## What we offer

- Founding equity and a competitive salary.
- Real compute, and the freedom to use it.
- Top-end hardware and tools for whatever the role requires.

## Apply

Email [careers@fermionresearch.com](mailto:careers@fermionresearch.com) with a link to something you have built, a few lines on the hardest model problem you have solved, and your CV or GitHub.

Source: [https://www.fermionresearch.com/careers/founding-researcher-engineer/](https://www.fermionresearch.com/careers/founding-researcher-engineer/)