Staff Machine Learning Engineer, Personalization
Spotify · New York, NY
Skills in this posting
Extracted from the posting text by the instrument — the demand side, read literally.
The 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.
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 personalization systems.
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.
You care deeply about creating high-quality user experiences through thoughtful application of machine learning.
You communicate effectively across technical and non-technical audiences, and you 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 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.
This team operates within the Eastern Standard time zone for collaboration.
Excerpt from the original listing. The full, current text lives at the source. Read and apply there →
The PivotHop read
- What a machine learning engineer actually earnsmedian, seniority, by country
- What machine learning engineers do insteadevery measured route out
- Data Scientist → Machine Learning Engineer52% readiness
- All open machine learning engineer rolesthe full board
Where these skills also reach
Adjacent occupations measured from the same postings — readiness is what a machine learning engineer’s profile already covers.
- 78 open research scientist roles70% readiness from machine learning engineer
- 250 open data scientist roles67% readiness from machine learning engineer
- 146 open ai engineer roles60% readiness from machine learning engineer
- 13 open data annotator roles53% readiness from machine learning engineer
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