Staff Machine Learning Engineer, Home Surfaces
New York, NY
- 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 setup
- Unconfirmed
- Employment
Permanent — employment source
Employment type Permanent
Read the full posting
What you’ll work on
Full postingOwn 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.
From the employer’s posting
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.
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.
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.
What you’ll bring
All qualificationsCore experience
- You have 8+ years of experience building and deploying machine learning systems in production environments.
- You have deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
- You have strong proficiency in Python and hands-on experience building machine learning systems with PyTorch.
- You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
Qualification wording
You have 8+ years of experience building and deploying machine learning systems in production environments.
You have deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
You have strong proficiency in Python and hands-on experience building machine learning systems with PyTorch.
You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
Tools in this posting
- Python
- BigQuery
- Airflow
- PyTorch
Source — Tool mentions in context
- You have deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms. - You have strong proficiency in Python and hands-on experience building machine learning systems with PyTorch. - You are experienced 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 are experienced building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms. Where You'll Be
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.
Surfaces Moments is a ML team within the Personalization Mission focused on creating moment-based experiences across Spotify surfaces. The team owns and evolves the experiences that help listeners quickly connect with the content that matters most to them, including the Home Shortcuts experience and the underlying intelligence that powers it. By combining cutting-edge machine learning, recommendation systems, and product thinking, the team delivers highly relevant, personalized experiences to millions of listeners around the world.
As a Staff Machine Learning Engineer, you will 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 that delight users and drive meaningful impact. This role is ideal for someone who enjoys taking models from research to production, driving technical direction in ambiguous problem spaces, and solving complex personalization challenges at global scale.
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.
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Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.
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Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale.
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Drive technical direction in ambiguous problem spaces and contribute to the long-term architecture of personalization systems.
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Mentor and support other machine learning engineers, helping raise the bar across the team.
Who You Are
-
You have 8+ years of experience building and deploying machine learning systems in production environments.
-
You have deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
-
You have strong proficiency in Python and hands-on experience building machine learning systems with PyTorch.
-
You are experienced 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.
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You care deeply about creating high-quality user experiences through thoughtful application of machine learning.
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You communicate effectively across technical and non-technical audiences, and you influence technical decisions beyond your immediate team
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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 are experienced 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.
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This team operates within the Eastern Standard time zone for collaboration.
Employment type
Permanent
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
Complete your application on jobs.lever.co. The employer’s form will show what is required.
Already applied? Track this application
Source & posting history
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.
- Status in our records
- Active
- First seen by us
- Sep 15, 2026
- Recorded sightings
- 12
- Last seen by us
- Oct 8, 2026
- Employer says posted
- Sep 10, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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