Senior ML Platform Engineer – Earth Data (Remote)

hum-ai; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Equity potential

Health, dental, vision insurance

Job summary

hum-ai in San Francisco is seeking a Senior Machine Learning Engineer to own the data-to-model stack, from ingest to training to inference, enabling researchers to ship ideas fast. You will build scalable ML pipelines, coordinate experiments, and ensure reliability at petabyte scale.

You will collaborate with engineers and scientists, implement benchmarks, optimize cost, and support an agent that consumes model outputs.

Qualifications

• Bachelor's degree in computer science, engineering, or a related field, or equivalent experience.
• 5+ years of relevant industry experience, including hands-on machine learning engineering.
• Proficiency building distributed, multi-node training pipelines in PyTorch and Lightning.
• Experience with AWS or another cloud platform, particularly the tooling for training and serving medium-to-large models.
• Strong software engineering fundamentals: you write production-quality Python and care about reliability, observability, and cost.
• Fluent with modern agentic coding tools (e.g. Claude Code, Cursor, Codex) and current on how to use them well.

Responsibilities

• Own the ML platform. Build and run the systems for experiment tracking, distributed training at scale, and serving inference, and keep them fast, reliable, and reproducible.
• Collaborate on the interfaces. Work with our engineers and scientists to define and maintain the boundaries between data and training, data and inference, and inference and the agent.
• Enable multimodal training. Build the data and model plumbing that lets us train on many sensors, observation types, and text in one model.
• Go from small to large. Help our scientists prove ideas in fast, cheap experiments, then scale the winners to petabyte-scale training runs.
• Make evaluation real. Implement and improve our benchmarks and evaluation frameworks, both internal and user-facing.
• Watch the meter. Measure and optimize the cost and wall-clock time of experiments, training, and inference.
• Support the agent. Work with the product team on how the agent calls the model and consumes its outputs, and build the integration.

Skills

ML engineering

Distributed training

Python programming

AWS experience

Education

Bachelor's degree in CS or related field

Tools

PyTorch

Lightning

AWS

Claude Code

Cursor

Codex

Job description

hum-ai in San Francisco is seeking a Senior Machine Learning Engineer to own the data-to-model stack, from ingest to training to inference, enabling researchers to ship ideas fast. You will build scalable ML pipelines, coordinate experiments, and ensure reliability at petabyte scale.

You will collaborate with engineers and scientists, implement benchmarks, optimize cost, and support an agent that consumes model outputs.