Staff Machine Learning Engineer, Home Surfaces
New
J
JobgetherContent discovery
Must be based in a North American region where the employer has an established work location, Eastern Time working hoursFull-TimeStaff
SalaryAnnual base salary: $227,495–$324,993 for US-based employees. Equity: Additional equity compensation on top of the base salary.
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Job Details
- Required Skills
- PythonMachine LearningPyTorchA/B testingLLM
Requirements
- Extensive experience developing, deploying, and operating machine learning systems in production.
- 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 with PyTorch.
- Practical experience training, fine-tuning, evaluating, and optimizing large language models, including SFT, distillation, and LoRA.
- Understanding of large-scale model inference, including latency, reliability, scalability, and cost optimization.
- Experience designing, implementing, and interpreting online experiments and A/B tests.
- Experience operating distributed machine learning workloads using Ray, FSDP, HSDP, or comparable technologies.
- Experience building and maintaining data pipelines and workflow orchestration systems using tools such as Flyte, Airflow, and BigQuery.
- Strong technical judgment and ability to influence architecture and engineering decisions beyond your immediate team.
- Excellent communication and collaboration skills with technical and non-technical stakeholders.
- Must be based in a North American region where the employer has an established work location and be available to collaborate within Eastern Time working hours.
Responsibilities
- Own, develop, and improve machine learning models and systems powering personalized home feeds, including the Shortcuts experience.
- Design, build, deploy, and optimize recommendation systems serving millions of users.
- Develop content recommendation capabilities for AI-powered and agentic user experiences.
- Train, fine-tune, evaluate, and optimize large language models using SFT, knowledge distillation, and parameter-efficient techniques including LoRA.
- Collaborate with product managers, software engineers, data scientists, and designers to define objectives and deliver personalization features.
- Design and execute A/B tests and online experiments to evaluate model performance and user outcomes.
- Establish model evaluation, monitoring, and optimization practices for relevance, reliability, latency, and cost efficiency.
- Improve machine learning platforms, data pipelines, orchestration workflows, and production infrastructure.
- Lead technical decisions and contribute to recommendation-system architecture and strategy.
- Mentor machine learning engineers and promote knowledge sharing and engineering standards.
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