Staff machine learning engineer, home surfaces

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.

Accountabilities

• 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.
• Lead technical decision-making in ambiguous problem spaces and contribute to the long-term architecture and strategy of recommendation systems.
• Mentor and support other machine learning engineers, promote knowledge sharing, and strengthen engineering standards and practices across the team.

Requirements:

• 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.
• Understanding of large-scale model inference, including latency management, reliability, scalability, and cost optimization.
• 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.
• Excellent communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.
• A user-focused mindset and a commitment to applying machine learning thoughtfully to improve content discovery and engagement.

Location and collaboration:

Must be based in a North American region where the employer has an established work location, with availability to collaborate within Eastern Time working hours.

Benefits:

• Annual base salary: $227,495–$324,993 for US-based employees.
• Equity: Additional equity compensation on top of the base salary.
• Health insurance: Health coverage as part of the employee benefits package.
• Paid parental leave: Six months of paid parental leave.
• Retirement savings: 401(k) retirement plan.
• Paid time off: 23 paid days off annually.
• Flexible holidays: 13 paid flexible holidays.
• Paid sick leave: Paid sick leave to support health and well-being.
• Monthly meal allowance: Financial support for meals.
• Flexible work arrangements: Location flexibility within eligible North American regions, subject to available work locations.
• Inclusive workplace: A commitment to equal opportunity, diversity, accessibility, and reasonable accommodations throughout the recruitment process.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!

Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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