# Inference at Genesis AI

> Onsite · United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Agenesis%3Abbbfdfda-ba1e-46be-8d6f-70313e68f928 (read 2026-11-06).

## Facts (observed)

- Company: [Genesis AI](https://alderhq.app/companies/genesis-ai)
- Location text: Bay Area
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: Bay Area")
- Posted: 2026-04-30 (189 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/genesis/bbbfdfda-ba1e-46be-8d6f-70313e68f928
- Skills listed (inferred): Python, Rust, C++, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 1495): p25 $161k, median $200k, p75 $247k

## Description (the employer's text)

### What You’ll Do

Build low-latency inference pipelines for on-device deployment, enabling real-time next-token and diffusion-based control loops in robotics

Design and optimize distributed inference systems on GPU clusters, pushing throughput with large-batch serving and efficient resource utilization

Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate it seamlessly into high-level frameworks

Optimize workloads for both throughput (batching, scheduling, quantization) and latency (caching, memory management, graph compilation)

Develop monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across both stacks

### What You’ll Bring

Deep experience in distributed systems, ML infrastructure, or high-performance serving (8+ years)

Production-grade expertise in Python, with strong background in systems languages (C++/Rust/Go)

Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory and compute scheduling

Proven track record scaling inference workloads in both throughput-oriented cluster environments and latency-critical on-device deployments

System-level mindset with a history of tuning hardware–software interactions for maximum efficiency, throughput, and responsiveness

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Agenesis%3Abbbfdfda-ba1e-46be-8d6f-70313e68f928 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.