Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
ML Infrastructure Engineer
South San Francisco · South San Francisco, California, United States
Open 4 months, first seen by us 127 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
R&D - Software — team source
Department: R&D - Software
From the employer’s posting
Before you apply
- Work authorization
- Must already have the right to work in the US
Legally authorized to work in the United States
What you’ll work on
Full postingWe’re looking for an experienced ML Infrastructure Engineer to join our team.
Develop and deploy scalable AI training and validation pipelines in the cloud.
Develop and deploy scalable AI training and validation pipelines in the cloud
Enable large-scale simulation-based evaluation of autonomy software and models
Optimize performance, I/O and GPU utilization
Deploy monitoring and CI/CD pipelines
Build tooling and dashboards for experimentation, orchestration and visualization
From the employer’s posting
We’re looking for an experienced ML Infrastructure Engineer to join our team. As an early member, you will play a pivotal role in architecting scalable cloud infrastructure for our AI and data pipelines. You'll need to thrive in a fast-paced startup environment where you'll wear multiple hats and have a direct impact on our product's evolution. Ideally, you have a proven track record of developing and deploying high-performance ML and cloud pipelines in production, and you're passionate about pushing the boundaries of what's possible in robotics with AI.
Develop and deploy scalable AI training and validation pipelines in the cloud.
Enable large-scale evaluation of autonomy software and models via simulations in the cloud.
Optimize performance, I/O and GPU utilization.
Deploy monitoring and CI/CD pipelines.
Build tooling and dashboards for rapid experimentation, orchestration and visualization.
What you’ll bring
All qualificationsCore experience
- 4+ years deploying high-performance ML pipelines in production
- Proficient in Python, comfortable with C++/Go
- Experience with PyTorch or similar ML frameworks
- Experience deploying on AWS, GCP or Azure
- Experience with Docker, Kubernetes and Airflow
Qualification wording
4+ years of experience deploying high-performance ML pipelines in production.
Proficient in Python and comfortable with C++/Go.
Experience with ML frameworks like PyTorch.
Experience with deploying on cloud platforms like AWS, GCP or Azure.
Experience with tooling like Docker, Kubernetes, and Airflow.
Posting record
Report an errorOpen 4 monthsWe first saw this posting 127 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 6, 2026127 days ago
- Recorded sightings
- 95
- Last seen by us
- Nov 10, 2026
- Employer says posted
- May 26, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 127
Up longer than most. Similar jobs usually come down between day 8 and day 75.
From 5,207 similar postings that came down. Coming down is not always a hire.
Gritt Robotics is hiring
3 open jobs
- No new data jobs since July
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- C++
- Python
- AWS
- Azure
- Docker
- GCP
- Kubernetes
- Airflow
- PyTorch
- Go
Where the posting names them
- 4+ years of experience deploying high-performance ML pipelines in production. - Proficient in Python and comfortable with C++/Go. - Experience with ML frameworks like PyTorch.
- Experience with IO and data-loading workflows, including formats like Parquet, HDF5, TFRecord etc. - Experience with deploying on cloud platforms like AWS, GCP or Azure. - Experience with tooling like Docker, Kubernetes, and Airflow.
- Experience with deploying on cloud platforms like AWS, GCP or Azure. - Experience with tooling like Docker, Kubernetes, and Airflow. - Should be comfortable taking ownership of tasks with light supervision.
- Proficient in Python and comfortable with C++/Go. - Experience with ML frameworks like PyTorch. - Experience with IO and data-loading workflows, including formats like Parquet, HDF5, TFRecord etc.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Applying through Ashby
- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
Gritt is an intelligent system that combines robotics and AI to build the infrastructure that pulls society forward. Gritt deploys via simple attachments to common equipment found on construction sites and autonomously performs labor-intensive tasks, verification, and planning. Gritt systems are already building critical infrastructure in the harshest outdoor environments, starting with large-scale solar. The founding team includes experts in robotics and AI from Carnegie Mellon, Stanford, and MIT. Gritt is backed by Obvious Ventures, Union Square Ventures, First Round Capital, Climactic, Congruent Ventures, and other leading firms.
Role: Software - ML Infrastructure Engineer
Location: SF Bay Area (in-person)
About the role
We’re looking for an experienced ML Infrastructure Engineer to join our team. As an early member, you will play a pivotal role in architecting scalable cloud infrastructure for our AI and data pipelines. You'll need to thrive in a fast-paced startup environment where you'll wear multiple hats and have a direct impact on our product's evolution. Ideally, you have a proven track record of developing and deploying high-performance ML and cloud pipelines in production, and you're passionate about pushing the boundaries of what's possible in robotics with AI.
What you’ll get to work on
Develop and deploy scalable AI training and validation pipelines in the cloud.
Spin up distributed pipelines for data ingestion, pre-processing, training and evaluation.
Deploy monitoring and CI/CD pipelines.
Enable large-scale evaluation of AI models via cloud-based metrics.
Enable large-scale evaluation of autonomy software and models via simulations in the cloud.
Optimize performance, I/O and GPU utilization.
Build tooling and dashboards for rapid experimentation, orchestration and visualization.
Work with other teams to integrate cloud tooling into workflows.
What we look for
Degree in computer science or related engineering disciplines (or equivalent experience).
4+ years of experience deploying high-performance ML pipelines in production.
Proficient in Python and comfortable with C++/Go.
Experience with ML frameworks like PyTorch.
Experience with IO and data-loading workflows, including formats like Parquet, HDF5, TFRecord etc.
Experience with deploying on cloud platforms like AWS, GCP or Azure.
Experience with tooling like Docker, Kubernetes, and Airflow.
Should be comfortable taking ownership of tasks with light supervision.
Must have excellent problem-solving skills.
Legally authorized to work in the United States.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
South San Francisco, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
- Must have excellent problem-solving skills. - Legally authorized to work in the United States.