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Goliath Partners

Senior Machine Learning Engineer

Goliath Partners
San Francisco, CA $2,000k - $2,000k/yr Full time Posted 2h ago
Artificial Intelligence Cloud Machine Learning Distributed Machine Learning Machine Learning Machine Learning Engineering Machine Learning Optimization Machine Learning Serving Software Development
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Job Description

Senior Machine Learning Engineer role focused on connecting ML Infrastructure Engineers to build and scale infrastructure for distributed model training and high-performance inference.

Responsibilities

  • Build and scale infrastructure powering distributed model training and high-performance inference for production AI systems
  • Develop scalable distributed training and inference infrastructure
  • Optimize system performance, reliability, latency, and throughput
  • Design and own production ML infrastructure at scale

Requirements

  • 5+ years of experience in ML Infrastructure, Distributed Systems, or Software Engineering, or equivalent demonstrated expertise
  • Strong programming skills in Python and C++ or Go
  • Hands-on experience with PyTorch, JAX, or similar ML frameworks
  • Experience building scalable distributed training and inference infrastructure
  • Strong knowledge of Kubernetes, Docker, AWS/GCP, and GPU compute environments
  • Proven ability to optimize system performance, reliability, latency, and throughput
  • Ability to design and own production ML infrastructure at scale
  • Experience supporting large-scale LLM or foundation model training
  • Expertise in CUDA, Triton, NCCL, or GPU performance optimization
  • Experience with distributed training frameworks such as Ray, DeepSpeed, or Megatron-LM
  • Contributions to high-performance ML systems or influential open-source infrastructure projects
  • Experience building infrastructure at leading AI research labs or high-growth technology companies

Technology Focus

  • Programming: Python, C++, Go
  • ML frameworks: PyTorch, JAX
  • Infrastructure & orchestration: Kubernetes, Docker
  • Cloud & compute: AWS, GCP, GPU environments
  • GPU optimization: CUDA, Triton, NCCL
  • Distributed training frameworks: Ray, DeepSpeed, Megatron-LM

Education

  • Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Mathematics, or a related technical field
  • PhD in Computer Science, Machine Learning, or Distributed Systems is a plus
  • Exceptional engineers with demonstrated technical impact and deep systems expertise are strongly encouraged to apply

What Sets You Apart

  • Experience supporting large-scale LLM or foundation model training
  • Expertise in CUDA, Triton, NCCL, or GPU performance optimization
  • Experience with distributed training frameworks such as Ray, DeepSpeed, or Megatron-LM
  • Contributions to high-performance ML systems or influential open-source infrastructure projects
  • Experience building infrastructure at leading AI research labs or high-growth technology companies

Location: San Francisco, CA (onsite)

Compensation: USD 2,000,000 - 2,000,000 per yearly

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