Job Requirements
Remote
Secret Polygraph not specified
Mid Level Career (5+ yrs experience)
$150,000 - $300,000
Job Description
NOTE:
This role can be remote in the US, but if you are in San Francisco, it is on site
Machine Learning Engineer
San Francisco, CA (SoMa) | Full-Time | Remote Considered
About the Opportunity
Our client is a mission-driven AI startup building next-generation forecasting infrastructure at the intersection of deep learning and geoscience. Their models run across a heterogeneous compute stack, public cloud, dedicated GPU clusters, edge-deployed hardware, and national supercomputing facilities, and their work directly supports federal defense and public safety missions. If you want your code to matter in the real world, this is worth a look.
What You''ll Do
Architect, train, and iterate on deep learning models purpose-built for complex scientific domains
Push model performance forward through GPU optimization and distributed training strategies
Ingest, validate, and transform massive geospatial datasets into clean, analysis-ready form
Build and own scalable data pipelines that move high-volume scientific data reliably end to end
Partner closely with domain scientists to embed ML into advanced simulation workflows
Write production-grade code: tested, readable, and built to last
Debug hard problems across distributed systems when they arise, and figure out why they happened
What You Bring
6+ years of software engineering experience, with meaningful time spent on ML systems
Python fluency is required; proficiency in C++, Java, or Rust is a strong plus
Real experience with PyTorch and a solid grasp of modern neural network architectures
Familiarity with scientific computing libraries (NumPy, SciPy) and time series methods
Hands-on work with geospatial data processing and big data pipelines
Cloud platform experience on AWS or Google Cloud Platform
Working knowledge of distributed computing, tensor operations, and GPU performance tuning
Full-stack exposure and comfort with databases are a plus
Who Thrives Here
This is an R&D-heavy environment with a lot of open questions and not a lot of playbooks. The engineers who do well here are self-directed, intellectually curious, and comfortable building in ambiguity. If you need a well-defined ticket queue to feel productive, this probably isn''t the right fit. If you like hard problems and want room to actually own your work, keep reading.
Our client runs a small team, roughly five full-time engineers plus contractors, led by a hands-on CTO. Everyone works across the full customer portfolio. They move fast but take code quality seriously, and they believe the best time to iterate is when the system is working, not when it''s on fire.
This role can be remote in the US, but if you are in San Francisco, it is on site
Machine Learning Engineer
San Francisco, CA (SoMa) | Full-Time | Remote Considered
About the Opportunity
Our client is a mission-driven AI startup building next-generation forecasting infrastructure at the intersection of deep learning and geoscience. Their models run across a heterogeneous compute stack, public cloud, dedicated GPU clusters, edge-deployed hardware, and national supercomputing facilities, and their work directly supports federal defense and public safety missions. If you want your code to matter in the real world, this is worth a look.
What You''ll Do
Architect, train, and iterate on deep learning models purpose-built for complex scientific domains
Push model performance forward through GPU optimization and distributed training strategies
Ingest, validate, and transform massive geospatial datasets into clean, analysis-ready form
Build and own scalable data pipelines that move high-volume scientific data reliably end to end
Partner closely with domain scientists to embed ML into advanced simulation workflows
Write production-grade code: tested, readable, and built to last
Debug hard problems across distributed systems when they arise, and figure out why they happened
What You Bring
6+ years of software engineering experience, with meaningful time spent on ML systems
Python fluency is required; proficiency in C++, Java, or Rust is a strong plus
Real experience with PyTorch and a solid grasp of modern neural network architectures
Familiarity with scientific computing libraries (NumPy, SciPy) and time series methods
Hands-on work with geospatial data processing and big data pipelines
Cloud platform experience on AWS or Google Cloud Platform
Working knowledge of distributed computing, tensor operations, and GPU performance tuning
Full-stack exposure and comfort with databases are a plus
Who Thrives Here
This is an R&D-heavy environment with a lot of open questions and not a lot of playbooks. The engineers who do well here are self-directed, intellectually curious, and comfortable building in ambiguity. If you need a well-defined ticket queue to feel productive, this probably isn''t the right fit. If you like hard problems and want room to actually own your work, keep reading.
Our client runs a small team, roughly five full-time engineers plus contractors, led by a hands-on CTO. Everyone works across the full customer portfolio. They move fast but take code quality seriously, and they believe the best time to iterate is when the system is working, not when it''s on fire.
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