Staff Machine Learning Engineer, Home Surfaces

Jobgether · Remote, United States

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Machine Learning Engineer, Home Surfaces based in the United States.

The Staff Machine Learning Engineer will shape personalized content discovery by developing advanced recommendation systems that help millions of listeners find relevant podcasts and episodes. This role sits at the intersection of machine learning, large language models, personalization, and large-scale production infrastructure. You will lead the development and optimization of models powering prominent digital experiences, including personalized home feeds and AI-driven discovery features. Working closely with product managers, engineers, data scientists, and designers, you will translate research into reliable, scalable solutions. You will influence technical strategy, drive experimentation, and improve recommendation quality, system performance, and cost efficiency. This is an opportunity to solve complex machine learning challenges while mentoring engineers and shaping the future of personalized user experiences.
\Own, develop, and continuously improve the machine learning models and systems powering personalized home feeds, including the Shortcuts experience.
Design, build, deploy, and optimize recommendation systems that serve millions of users globally.
Develop content recommendation capabilities for emerging AI-powered and agentic user experiences.
Train, fine-tune, evaluate, and optimize large language models using approaches such as supervised fine-tuning (SFT), knowledge distillation, and parameter-efficient training techniques, including LoRA.
Collaborate with product managers, software engineers, data scientists, and designers to define product objectives, establish experimentation strategies, and deliver effective personalization features.
Design and execute A/B tests and online experiments to evaluate model performance, measure user outcomes, and guide iterative improvements.
Establish robust model evaluation, monitoring, and optimization practices to improve recommendation relevance, reliability, latency, and cost efficiency.
Enhance machine learning platforms, data pipelines, orchestration workflows, and production infrastructure to support large-scale personalization.
Mentor and support other machine learning engineers, promote knowledge sharing, and strengthen engineering standards and practices across the team.
Extensive experience developing, deploying, and operating 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 solutions using PyTorch.
Practical experience training, fine-tuning, evaluating, and optimizing large language models, including techniques such as SFT, distillation, and LoRA.
Experience designing, implementing, and interpreting online experiments and A/B tests to measure impact and improve user experiences.
Experience operating distributed machine learning workloads using frameworks such as Ray, FSDP, HSDP, or comparable technologies.
Experience building and maintaining data pipelines and workflow orchestration systems using tools such as Flyte, Airflow, BigQuery, and cloud-based storage platforms.
Strong technical judgment and the ability to influence architecture and engineering decisions beyond your immediate team.
A user-focused mindset and a commitment to applying machine learning thoughtfully to improve content discovery and engagement.
Equity: Additional equity compensation on top of the base salary.
Health insurance: Health coverage as part of the employee benefits package.
Retirement savings: 401(k) retirement plan.
Flexible holidays: 13 paid flexible holidays.
Financial support for meals.
Flexible work arrangements: A commitment to equal opportunity, diversity, accessibility, and reasonable accommodations throughout the recruitment process.
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