# Research Engineer at Kovari

> Onsite · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Akovari%3A9fe668d5-cd97-4a63-9c46-76bcb50a4920 (read 2026-11-11).

## Facts (observed)

- Company: [Kovari](https://alderhq.app/companies/kovari)
- Location text: San Francisco
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco")
- Posted: 2026-09-27 (44 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/kovari/9fe668d5-cd97-4a63-9c46-76bcb50a4920

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 544): p25 $200k, median $249k, p75 $300k

## Description (the employer's text)

### About Kovari

Kovari develops frontier technology to transform physical businesses. We partner with established operators to change how they run, and we build and operate businesses of our own through our subsidiaries.

We are a small team of builders, operators, and researchers. We deploy on site, alongside the businesses we serve, and iterate in the field. We have raised over $6M from leading investors.

We are based in San Francisco and work in person.

### The Role

You will research and ship manipulation policies that work reliably in the field. You own the loop from data to model to deployment, and you see your policies run on robots in live operations.

### What You'll Do

Research and develop high-reliability manipulation policies built for fast deployment and iteration

Operate in a fast data flywheel across multiple modalities

Deep debug failure modes in transformer and diffusion policies deployed in the field

Optimize policies for real-time (~10 Hz) inference on edge hardware

### What You Bring

Experience deploying robot policies on hardware (model-based, RL, or imitation learning)

Sim-to-real or real robot data experience

Experience with multimodal inputs: vision, depth, force/torque, proprioception

Edge inference optimization with TensorRT, ONNX Runtime, or TVM

CUDA kernel optimization

Bonus: publications at CoRL, RSS, ICRA, NeurIPS, or similar

Bias toward action: train fast, deploy early, learn from failure

### Values

Pace of learning trumps everything.

Relentless refinement of our craft.

Low ego, high ownership.

All-in on the mission. We work in person, and this isn't a 9-to-5.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Akovari%3A9fe668d5-cd97-4a63-9c46-76bcb50a4920 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.