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.
Machine Learning Engineer (Mid-Level)
San Francisco, California, United States
Actively hiring, first seen by us 3 hours ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FullTime
From the employer’s posting
What you’ll work on
Full postingBuild and deploy production machine learning systems in a fast-moving product environment.
Build and deploy production machine learning systems in a fast-moving product environment.
Design, train, and evaluate machine learning models for production use cases.
You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that address real business needs.
From the employer’s posting
Build and deploy production machine learning systems in a fast-moving product environment. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that address real business needs.
About the Role Build and deploy production machine learning systems in a fast-moving product environment. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that address real business needs. What You'll Do
What You'll Do Design, train, and evaluate machine learning models for production use cases. Build end-to-end ML pipelines, from data preprocessing through model serving and monitoring.
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 11, 2026today
- Recorded sightings
- 1
- Employer says posted
- Nov 7, 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 0
Early. Similar jobs usually come down between day 8 and day 75.
From 5,188 similar postings that came down. Coming down is not always a hire.
clera is hiring
16 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee 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.
- Python
- AWS
- Docker
- GCP
- Kubernetes
- SageMaker
- PyTorch
- scikit-learn
- TensorFlow
Where the posting names them
- Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation. - Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn. - Experience implementing production ML pipelines that cover data preprocessing, model serving, and monitoring.
- Experience implementing production ML pipelines that cover data preprocessing, model serving, and monitoring. - Experience deploying and maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker. - Experience with production A/B testing or experimentation frameworks, and optimizing models based on results.
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
About the Role
Build and deploy production machine learning systems in a fast-moving product environment. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that address real business needs.
What You'll Do
Design, train, and evaluate machine learning models for production use cases.
Build end-to-end ML pipelines, from data preprocessing through model serving and monitoring.
Work with product and engineering partners to turn business requirements into ML solutions.
Debug and improve production model performance using monitoring data and real-world feedback.
Write maintainable code and contribute to ML infrastructure and tooling.
Participate in code reviews and share technical knowledge with teammates.
What We're Looking For
At least 3 years of professional machine learning or software engineering experience, including hands-on ownership of production ML systems in a startup or fast-moving product environment.
Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation.
Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
Experience implementing production ML pipelines that cover data preprocessing, model serving, and monitoring.
Experience deploying and maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
Experience with production A/B testing or experimentation frameworks, and optimizing models based on results.
A completed university degree and professional employment experience after graduation.
Ability to work through ambiguity and prioritize practical impact in a dynamic environment.
Location
On-site in San Francisco, California.
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
San Francisco, California, United States
Location On-site in San Francisco, California.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.