Staff Machine Learning Engineer, Personalization

1 мес. назад

USALeadRemote

machine learningrecommendation systemslarge language modelsproduction-scale machine learning infrastructure

Lead machine learning engineering for personalized music and podcast recommendations at .

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 '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 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 system

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 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 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.

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.

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.