---
title: 'Machine Learning Engineer - Kernels at Mindbeam'
canonical: 'https://feeny.ai/job/machine-learning-engineer-kernels-mindbeam-united-states-dhdf0cnzf4ft'
type: 'job'
last_seen: '2026-09-06'
---

# Machine Learning Engineer - Kernels at Mindbeam

- **Company:** Mindbeam
- **Location:** United States
- **Compensation:** $150k–$190k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-05-21
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/mindbeam/c9e37111-597c-4247-bf5a-1083cf72ef39/application

**Skills:** C++, CUDA, GPU programming, Parallel computing, Systems-level optimization, ML frameworks, Profiling tools, Performance diagnostics, ROCm, TPU

> Design and implement custom GPU/accelerator kernels to maximize performance for next-generation AI workloads. Profile, benchmark, and optimize critical ML systems while collaborating with researchers to translate algorithmic advances into efficient production-ready code.

## Job description

## About Mindbeam

We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level.

Mission

Push the boundaries of performance by developing custom kernels and low-level optimizations for next-generation AI workloads.

## Role Expectations

- Design and implement custom GPU/accelerator kernels to maximize performance.
- Profile, benchmark, and optimize critical ML workloads.
- Collaborate with researchers to translate algorithmic advances into efficient, production-ready code.
- Stay current with hardware advancements (CUDA, ROCm, TPU) to inform kernel design.
- Document and share best practices for low-level optimization.

Background

- Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field—or equivalent experience.
- 2+ years of experience in GPU programming, parallel computing, or systems-level optimization.
- Strong coding skills in C++, CUDA, or similar languages.
- Familiarity with ML frameworks and their low-level backends.
- Experience optimizing workloads for distributed and heterogeneous compute environments.
- Comfort with profiling tools and performance diagnostics.

## About You

You are detail-oriented, performance-obsessed, and excited by the challenge of squeezing out every ounce of compute efficiency. You enjoy working at the intersection of algorithms and hardware, and you thrive in a collaborative environment where bold ideas are encouraged.

## About Mindbeam

## Company Overview
- **One-liner**: Mindbeam builds next-generation AI infrastructure, primarily through its Litespark framework that accelerates LLM training and inference while dramatically reducing energy consumption.
- **Entity Type**: Private (Bootstrapped – has not raised any funding)
- **Headquarters**: New York City, United States
- **Founded**: 2024
- **Founders**: Nii Osae (Founder & CEO)

## Core Business
- Primary industry/industries: AI Infrastructure, Machine Learning, LLM Optimization, Generative AI, Enterprise Software, Energy Management
- Target customers: B2B; enterprises and research labs training large language models; organizations looking to reduce GPU costs and energy consumption
- Mission or purpose statement: Building next-generation AI infrastructure (per LinkedIn)

## Products & Services
- **[Litespark]**: High-performance LLM framework that speeds up training and inference while improving GPU efficiency. A drop-in replacement for PyTorch that requires zero code changes. Key metrics: up to 6x higher throughput per GPU, up to 83% lower energy consumption, up to 88% higher MFU for multi-node training. Compatible with NVIDIA GPUs and CPUs (including Apple Silicon M5, AVX-512, Intel Core Ultra).
- **[Litespark-Inference]**: Inference engine that runs large models on standard CPUs without GPUs, achieving 2x faster performance on Apple Silicon M5, AVX-512, and Intel Core Ultra platforms.
- **[SpinGQE]**: A generative quantum eigensolver for spin Hamiltonians (open-source research project on GitHub).

## Market Standing
- **Valuation/Market Cap**: Not applicable (bootstrapped, no funding rounds)
- **Key Metric**: No revenue disclosed; company is unfunded with ~8-9 employees.
- **Notable Investors/Partners**: No investors. Partnerships appear through AWS (spotlighted by AWS Startups, presented at AWS NYC Summit). No formal partnership announcements.
- **Growth Signals**: 
  - Monthly website traffic grew +643.9% (to 2,336 visits) per LinkedIn.
  - LinkedIn followers: 739 as of mid-2026.
  - Active recruiting: multiple open positions listed on Ashby careers page.
  - Technical report released in October 2025 with benchmark results.
  - Active GitHub with three public repositories.
  - Featured by AWS Startups (August 2025).

## Competitive Advantages
- **Performance without code changes**: Litespark is a zero-code PyTorch drop-in – integrates with existing PyTorch and NVIDIA workflows, reducing adoption friction.
- **Dramatic energy and cost savings**: Up to 83% less energy and 6x faster training on same hardware translates directly to lower infrastructure costs.
- **CPU inference capability**: Enables running 2B parameter models on standard CPUs, expanding deployment options.
- **Strong early benchmarks**: Public benchmarks show measurable gains over baseline, which is rare for a young bootstrapped company.

## Strategic Focus
- Continuing to refine and commercialize Litespark for large-scale pre-training and inference.
- Building out the team (hiring across research, engineering, and operations).
- Expanding AWS Marketplace presence (GitHub repo for AWS Marketplace usage instructions).
- Research into quantum computing (SpinGQE) suggests long-term R&D ambition.

## Why Work Here
- **Culture**: Small, early-stage team (~8 employees) with a high density of talent from top organizations (Amazon Web Services, JPMorgan Chase, Cleveland Clinic, UC Berkeley, etc.). Emphasis on breakthrough performance and energy efficiency.
- **Remote/Hybrid/Office**: Not explicitly stated. Headquarters in New York City; likely hybrid given NYC base.
- **Notable Perks**: Opportunity to work on cutting-edge AI infrastructure with significant impact on cost and energy. Founding team access. Open source contributions.
- **Engineering Culture**: Research-heavy (two research scientists on staff), open-source friendly (GitHub), and hands-on with GPU and system optimization.

## Sources
1. [mindbeam.ai](https://mindbeam.ai)
2. [LinkedIn - Mindbeam AI](https://www.linkedin.com/company/mindbeam-ai)
3. [GitHub - Mindbeam-AI](https://github.com/mindbeam-ai/)
4. [Tracxn - Mindbeam](https://tracxn.com/d/companies/mindbeam/__88E92mpJA3ms2zlbSYwBFJQWzM1iaZ4IbxzK6Cz76Ng)
5. [Ashby Careers - Mindbeam](https://jobs.ashbyhq.com/mindbeam)

## Other roles at Mindbeam

- [Solutions Architect - Contractor](https://feeny.ai/job/solutions-architect-contractor-mindbeam-melbourne-3ehbnnwmwc2h) — Melbourne, Australia