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Software Engineer: ML Optimization

Summary by AIEngineer.careers

Software Engineer: ML Optimization is a ML Systems Engineer role (full-time). with Generalist. in SOMERVILLE, US. Compensation shown: $260K–$350K. Imported from Ashby-hosted careers page. Apply on the employer's site (jobs.ashbyhq.com).

Imported from Ashby-hosted careers page · Apply on jobs.ashbyhq.com

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SOMERVILLE · US
$260K–$350K
Full-time
MID
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The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.

Imported job description

Sourced from jobs.ashbyhq.com

Generalist builds embodied foundation models for robotics, starting with dexterity, and is seeking a Software Engineer for ML Optimization. You will own optimizations across both the training and on-robot inference stacks, driving step-function gains in performance. The role spans CUDA kernels, ML architecture, and system bottlenecks from NVLink and comms to Python inefficiencies.

Responsibilities

  • Optimize GPU performance across training and inference stacks
  • Implement ML, hardware, and software changes that yield step-function gains
  • Profile and remove bottlenecks from CUDA kernels, ML architecture, networking, CPU, and Python code

Requirements

  • Proficiency with the latest ML techniques for training and inference optimization in transformer and diffusion architectures
  • Strong systems and performance engineering skills across GPU, CPU, and networking
  • Fluency in Python, PyTorch, NumPy, and CUDA

Nice to Have

  • Experience with embodied or robotics ML systems
  • Background in large-scale distributed training

Tech Stack

CUDADiffusion ModelsGPU OptimizationNumPyNVLinkPythonPyTorchTransformer
Generalist

Generalist

San Mateo · US · 115+ employees

Generalist AI is a Series B robotics company based in San Mateo, California, founded in 2024 by Andy Zeng, Pete Florence, and Andrew Barry. The company develops robots that use integrated data, AI models, and hardware to perform a variety of physical tasks, training sensorimotor policies that respond to real-world stimuli. It has raised $700M in funding with a current valuation of $3B.

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