Senior Machine Learning Engineer

Remote (United States)

Work Arrangement: PST overlap required

Status: Full-Time

Compensation: $221,000 - $405,000 annually, excluding equity

About the Role

This opportunity is for a Senior Machine Learning Engineer to design, build, and maintain large-scale machine learning systems that directly shape user experience across a distributed social platform. The role focuses on recommendation systems, search optimization, spam detection, and automated labeling, with full ownership of production-grade models operating at scale.

What You’ll Do

• Design and implement machine learning models to improve personalized recommendations, search relevance, spam detection, and topic classification.
• Work with large-scale datasets, including complex social graph data and user-generated content.
• Run experiments and A/B tests, document findings, and iterate on model performance.
• Ensure algorithms deliver accurate recommendations and optimized user experiences.
• Train, retrain, and monitor models to maintain long-term effectiveness.
• Deploy and operate machine learning systems in high-scale production environments.
• Collaborate with engineering teams to integrate ML systems into distributed architecture.

Qualifications

• 3+ years of recent experience in machine learning or data science, with emphasis on recommendation systems and search.
• Strong proficiency in Python and hands-on experience with PyTorch or similar ML frameworks.
• Ability to rapidly prototype and experiment with models.
• Solid understanding of data structures, data modeling, and software architecture.
• Experience working with high-scale distributed systems.
• Experience in recommendation systems, user behavior modeling, or social graph analysis preferred.
• Familiarity with Golang is a plus.
• Experience with tools such as Ray, BigQuery, or Postgres preferred.
• Ability to extend and optimize machine learning libraries and frameworks.
• Strong communication skills and collaborative mindset.

Additional Information

Regular overlap with Pacific Time working hours is required. Proximity to Seattle and willingness to travel occasionally is considered a plus. There may be an on-site component during the interview process.

Compensation is determined based on geographic location, experience, skill set, training, certifications, and other relevant factors. Equity may be included as part of the total compensation package.