# Staff Machine Learning Engineer, Personalization

**Company:** [Spotify](https://hotfix.jobs/companies/spotify)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 8+ years
**Skills:** Machine Learning, Recommendation Systems, LLMs, PyTorch, Python, sft, distillation, lora, A/B Testing, Ray, flyte, Airflow, BigQuery
**Posted:** 2026-07-30

> Staff ML Engineer on Spotify's Personalization team, owning ML models and systems for the Home feed and Shortcuts experience. Build and productionize personalized recommendation systems and agentic AI experiences using LLMs, PyTorch, and large-scale infrastructure; drive experimentation, optimization, and technical direction while mentoring engineers.

## Job Description

## 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

- 8+ years of experience building and deploying machine learning systems in production environments.
- Deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
- Strong proficiency in Python and hands-on experience building machine learning systems with PyTorch.
- Experienced with large language model training, fine-tuning, evaluation, and optimization techniques including SFT, distillation, and LoRA.
- Worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization.
- Care deeply about creating high-quality user experiences through thoughtful application of machine learning.
- Communicate effectively across technical and non-technical audiences, and influence technical decisions beyond your immediate team.
- Know how to design, execute, and interpret online experiments and A/B tests to improve user outcomes.
- Experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
- Experienced building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms.

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