Staff Software Engineer, Machine Learning

We’re currently looking to hire a Staff Software Engineer on our Discovery/Engagement ML team. This team applies Machine Learning to unlock impactful and new possibilities that drive user engagement, growth, understanding and personalization. We design and implement end-to-end ML solutions that help users discover and navigate the rich, diverse content across our platform. If this excites you, keep reading!

What You'll Be Doing

• Raise the technical bar for the team by setting architectural direction, tackling systemic challenges, and identifying opportunities for innovation and efficiency.
• Design, build and scale robust, high-throughput, low latency recommender systems that power product features used by tens of millions of users every day.
• Build and deploy advanced ML models, leveraging deep learning, reinforcement learning, and optimization to drive engagement and enhance user experience.
• Collaborate cross-functionally to shape ML-driven product roadmaps, balancing speed of iteration with long-term system complexity and scalability.

What you should have

• 8+ years of experience in applied Machine Learning, inclusive Ph.D. or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Deep expertise in mainstream RecSys model architecture (e.g. two-tower, transformer-based model, multi-task learning, wide and deep etc.).
• Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
• Extensive experience building performant machine learning systems at scale and have driven execution from ideation to production implementation.
• Strong product intuition and a passion for building ML applications grounded in user feedback and real-world impact.
• Excellent communication and collaboration skills—able to lead complex, cross-functional technical initiatives and keep stakeholders aligned through clear updates and problem-solving.
• The ability to thrive in ambiguous environments, and are energized by tackling open-ended, technically challenging problems.

Bonus Points If You Have:

• Experience with whole-page optimization in marketplace or multi-objective ranking environments.
• Worked on notification systems or applied reinforcement learning in production settings.
• Built models for user representation learning or user modeling to power personalization.
• Deep expertise in distributed training (e.g. PyTorch DDP, Ray Train) and large-scale data processing pipelines (e.g., Spark, Flink).

This position is US-based and can be remote but if you live in the Bay Area, you’re welcome to work from our beautiful SF office.

The US base salary range for this full-time position is $272,000 to $306,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.