Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Staff Machine Learning Engineer
US - Remote
Open 5 months, first seen by us 155 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
Remote — work setting source
Listed location: US - Remote
Read the full posting- Employment
Employment terms need review — employment source
Experience bridging model development with real-time serving systems. This is a full-time role that can be held from one of our US offices or remotely in the United States. Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingFetch is building the future of personalized consumer experiences.
In this role, you will own the technical direction and execution for your team’s ML systems - driving high-quality architecture, guiding implementation, and ensuring models and infrastructure operate reliably at scale.
Own end-to-end architecture for team ML services, from training to serving
Design, build, and scale ML systems for personalization, ranking, search, or ads
Lead design reviews weighing latency, reliability, and experimentation tradeoffs
Translate ambiguous product needs into scalable technical solutions
Mentor engineers through hands-on technical guidance and feedback
From the employer’s posting
Fetch is building the future of personalized consumer experiences. We’re looking for a Staff Machine Learning Engineer to serve as the technical lead for a high-impact ML team focused on personalization, relevance, and ranking.
In this role, you will own the technical direction and execution for your team’s ML systems - driving high-quality architecture, guiding implementation, and ensuring models and infrastructure operate reliably at scale. You’ll partner closely with product and cross-functional stakeholders while remaining deeply hands-on in design and development.
Own end-to-end architecture for your team’s services, including model training, evaluation, deployment, and serving.
Design, build, and scale ML systems supporting personalization, ranking, search, or ad-related use cases.
Lead design reviews and ensure thoughtful tradeoffs around latency, reliability, experimentation, and maintainability.
Drive clarity in ambiguous problem spaces, translating product needs into scalable technical solutions.
Mentor engineers through hands-on technical guidance, feedback, and example.
What you’ll bring
All qualificationsCore experience
- 12+ years of industry experience in machine learning or software engineering, with demonstrated ownership of production ML systems operating at scale.
- Proven experience building and scaling ML systems in personalization, relevance, search, or ad tech domains.
- Experience deploying ML models into production and operating them at consumer scale.
- Demonstrated ownership of complex technical initiatives within a team.
- Experience mentoring engineers and influencing technical standards within a team.
- Ability to operate effectively in ambiguous environments and drive projects to completion.
Preferred experience
- Familiarity with LLMs and their application in personalization, feature generation, or search.
- Experience with real-time or streaming ML systems.
- Experience bridging model development with real-time serving systems.
Qualification wording
12+ years of industry experience in machine learning or software engineering, with demonstrated ownership of production ML systems operating at scale.
Proven experience building and scaling ML systems in personalization, relevance, search, or ad tech domains.
Experience deploying ML models into production and operating them at consumer scale.
Demonstrated ownership of complex technical initiatives within a team.
Experience mentoring engineers and influencing technical standards within a team.
Ability to operate effectively in ambiguous environments and drive projects to completion.
Familiarity with LLMs and their application in personalization, feature generation, or search.
Experience with real-time or streaming ML systems.
Experience bridging model development with real-time serving systems.
Education & alternatives
- Ability to operate effectively in ambiguous environments and drive projects to completion. - Bachelor’s degree in Computer Science, Engineering, or a related technical field. Preferred Requirements
Posting record
Report an errorOpen 5 monthsWe first saw this posting 155 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
- Jun 2, 2026155 days ago
- Recorded sightings
- 185
- Last seen by us
- Nov 4, 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 155
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
Fetch is hiring
29 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is picking up
- Takes jobs down at a usual pace
See how this role fits your experience
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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
Fetch is building the future of personalized consumer experiences. We’re looking for a Staff Machine Learning Engineer to serve as the technical lead for a high-impact ML team focused on personalization, relevance, and ranking.
In this role, you will own the technical direction and execution for your team’s ML systems - driving high-quality architecture, guiding implementation, and ensuring models and infrastructure operate reliably at scale. You’ll partner closely with product and cross-functional stakeholders while remaining deeply hands-on in design and development.
Role Responsibilities
- Serve as the technical lead for a single ML-focused team, setting direction and raising the bar on engineering quality and system design.
- Design, build, and scale ML systems supporting personalization, ranking, search, or ad-related use cases.
- Own end-to-end architecture for your team’s services, including model training, evaluation, deployment, and serving.
- Drive clarity in ambiguous problem spaces, translating product needs into scalable technical solutions.
- Lead design reviews and ensure thoughtful tradeoffs around latency, reliability, experimentation, and maintainability.
- Partner closely with product, data, and engineering stakeholders to deliver measurable business impact.
- Mentor engineers through hands-on technical guidance, feedback, and example.
- Use AI tools to accelerate development and improve system design, including:
- Prototyping and validating ideas with LLM tools.
- Leveraging AI for code iteration and experimentation.
- Using AI assistants for architecture diagramming and design validation.
- Exploring LLM-powered features where appropriate.
Minimum Requirements
- 12+ years of industry experience in machine learning or software engineering, with demonstrated ownership of production ML systems operating at scale.
- Proven experience building and scaling ML systems in personalization, relevance, search, or ad tech domains.
- Strong hands-on expertise in distributed systems, data pipelines, and ML infrastructure.
- Experience deploying ML models into production and operating them at consumer scale.
- Demonstrated ownership of complex technical initiatives within a team.
- Strong systems design skills with the ability to clearly articulate tradeoffs and implementation decisions.
- Experience mentoring engineers and influencing technical standards within a team.
- Ability to operate effectively in ambiguous environments and drive projects to completion.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
Preferred Requirements
- Familiarity with LLMs and their application in personalization, feature generation, or search.
- Experience with real-time or streaming ML systems.
- Exposure to experimentation frameworks (A/B testing) and model performance measurement.
- Experience bridging model development with real-time serving systems.
This is a full-time role that can be held from one of our US offices or remotely in the United States.
Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.
Source details
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- Pay
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- Location & working pattern
US - Remote
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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