Staff Machine Learning Engineer, Home Surfaces

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.