Machine Learning Systems Engineer

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Machine Learning Systems Engineer focusing on performance profiling and optimization for scalable ML at Motional. Collaborating on GPU kernel development and distributed training pipelines.

Posted 7/30/2026full-timeBoston • Massachusetts, Nevada, Pennsylvania • 🇺🇸 United StatesMid-LevelSenior💰 $144,000 - $192,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in optimizing machine learning workflows through advanced techniques in Python and PyTorch, with a strong foundation in GPU kernel design and data loading strategies to enhance training efficiency.

Highest-signal resume keywords

Python ProficiencyExtensive Experience With PyTorchGPU Kernel Design In Triton Or CUDAOptimization Of Distributed Training PipelinesAnalytical And Problem-Solving Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Profiling ToolsKernel FusionShardingTilingMachine Learning ConceptsModel Execution OptimizationData Loading OptimizationGradient ComputationCommunication OptimizationDistributed Training

Soft Skills

Analytical SkillsProblem-Solving SkillsBias For ActionData-Driven Approach

Tools & Technologies

NsightPyTorch ProfilerPyTorch Distributed

Industry Keywords

Computer ScienceComputer EngineeringMachine Learning Workloads

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time.
Optimize distributed training pipelines using frameworks such as PyTorch Distributed.
Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.
Optimize robust data loading pipelines that maximize training throughput.

Requirements

What you’ll need

Bachelor’s, Master’s degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.
Strong proficiency in Python.
Extensive hands-on experience with PyTorch.
Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.
Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges.

Benefits

Comp & perks

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401k with a company match
health saving accounts
life insurance
pet insurance
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