Pay
Higher than most similar jobs
$227K–$325K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Staff Machine Learning Engineer, Home Surfaces
New York, NY
Live, first seen by us 15 days ago- Pay
$227,495–324,993/yearAnnual period assumed. Plus equity — pay source
This team operates within the Eastern Standard time zone for collaboration. The United States base range for this position is $227,495- $324,993 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future. Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
Read the full posting- Work setting
- Not stated
- Employment
Permanent — employment source
Employment type Permanent
Employment type in the employer’s posting: Permanent
From the employer’s posting
What you’ll work on
Full postingOwn and improve the ML models behind the Home feed and Shortcuts
Design, build, and ship personalized recommendations for listeners globally
Train, fine-tune, and optimize large language models (SFT, distillation)
Drive A/B testing and monitoring of recommendation quality and cost
Mentor other Machine Learning Engineers on the team
From the employer’s posting
Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience.
Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally.
Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches.
Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.
Mentor and support other Machine Learning Engineers, sharing knowledge and helping strengthen engineering practices across the team.
What you’ll bring
All qualificationsCore experience
- You have deep experience building and deploying machine learning systems in production environments.
- You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
- You are proficient in Python and have hands-on experience building machine learning systems with PyTorch.
- You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA.
- You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
- You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms.
Qualification wording
You have deep experience building and deploying machine learning systems in production environments.
You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
You are proficient in Python and have hands-on experience building machine learning systems with PyTorch.
You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA.
You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 19, 202615 days ago
- Recorded sightings
- 25
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 21, 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 15
Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
Spotify is hiring
15 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
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
- BigQuery
- Airflow
- PyTorch
Where the posting names them
- You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms. - You are proficient in Python and have hands-on experience building machine learning systems with PyTorch. - You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA.
- You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks. - You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms. Where You'll Be
Your next step
Complete your application on jobs.lever.co. The employer’s form will show what is required.
Applying through Lever
- No account needed: one form, and Lever reads your resume into the profile recruiters search.
Already applied? Track this application
Has Spotify posted this job before?
Yes. We’ve followed one other Staff Machine Learning Engineer, Home Surfaces job there since September 2026. It is still up.
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
The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.
The Surfaces Podcasts team owns podcast recommendations across some of Spotify’s most visible surfaces, including Home and the Now Playing view. We build and improve the recommendation systems that help listeners discover their next favorite show or episode while helping creators reach the right audiences.
As a Staff Machine Learning Engineer, you’ll help shape the future of personalized discovery and engagement at Spotify. You’ll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences for listeners at global scale. You’ll take models from research to production, help set technical direction in ambiguous problem spaces, and solve complex personalization challenges across Spotify.
What You'll Do
- Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience.
- Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally.
- Build content recommendation systems for emerging agentic and AI-powered user experiences.
- Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches.
- Partner closely with Product Managers, Engineers, Data Scientists, and Designers to define and execute experimentation strategies.
- Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.
- Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale.
- Drive technical direction in ambiguous problem spaces and contribute to the long-term architecture of our personalization systems.
- Mentor and support other Machine Learning Engineers, sharing knowledge and helping strengthen engineering practices across the team.
Who You Are
- You have deep experience building and deploying machine learning systems in production environments.
- You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
- You are proficient in Python and have hands-on experience building machine learning systems with PyTorch.
- You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA.
- You have worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization.
- You care about creating high-quality user experiences through thoughtful applications of machine learning.
- You communicate effectively with technical and non-technical partners and can influence technical decisions beyond your immediate team.
- You know how to design, execute, and interpret online experiments and A/B tests to improve user outcomes.
- You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
- You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms.
Where You'll Be
- We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location.
- This team operates within the Eastern Standard time zone for collaboration.
The United States base range for this position is $227,495- $324,993 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
Employment type
Permanent
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
This team operates within the Eastern Standard time zone for collaboration. The United States base range for this position is $227,495- $324,993 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future. Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
- Location & working pattern
New York, NY
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.