Senior Machine Learning Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Take ownership of the ML platform, encompassing experiment tracking, large-scale distributed training, and inference serving.

• Develop and sustain efficient, dependable, and reproducible ML systems.

• Establish and uphold interfaces between data and training, data and inference, as well as inference and the agent.

• Create data and model pipelines for multimodal training that spans various sensors, observation types, and text.

• Assist research scientists in validating concepts through swift, cost-effective experiments, and scale successful methodologies for petabyte-scale training runs.

• Implement and enhance both internal and user-facing benchmarks and evaluation frameworks.

• Assess and optimize costs and wall-clock time associated with experiments, training, and inference.

• Collaborate with product engineers on how the agent interacts with models and utilizes outputs.

• Develop integrations between models and agents.

• Partner with engineering teams and AI research scientists to ensure models are trainable, measurable, and servable at scale.

⛳️ Requirements

• Bachelor’s degree in computer science, engineering, or a related discipline, or equivalent experience.

• Over 5 years of pertinent industry experience, including practical machine learning engineering.

• Expertise in constructing distributed, multi-node training pipelines utilizing PyTorch and Lightning.

• Familiarity with AWS or another cloud platform, especially tools for training and serving medium to large models.

• Strong foundation in software engineering and production-quality Python programming.

• Focus on reliability, observability, and cost-effectiveness.

• Proficiency with modern coding tools for agents such as Claude Code, Cursor, or Codex.

• Experience in training large diffusion or transformer models, particularly with video or time-series data.

• Background in multimodal or spatiotemporal data, geospatial data, or geoscience challenges.

• Experience deploying models in production and integrating them with downstream applications or agents.

• Experience in a startup environment and comfort working within a small, dynamic team.

🏝️ Benefits

• Significant early-stage equity opportunities.

• Comprehensive health, dental, and vision coverage.

• Competitive salary package.

• Engaging work with a positive impact, supporting nature conservation, carbon removal, and government clients.

• Collaboration within a small, technical, and cooperative team.

• Support and partnerships from investors and organizations including AWS and the United Nations.