Senior Machine Learning Infrastructure Engineer, Embedding Platform

Responsibilities

• Design, train, and improve large-scale machine learning platforms for recommendation or personalization systems.
• Own and deliver major ML system components end to end, from problem framing through production rollout.
• Build and optimize end-to-end ML pipelines spanning data preparation, feature generation, training, evaluation, and deployment.
• Improve distributed training, model efficiency, and online inference performance.
• Apply modern modeling approaches, including sequence modeling and related foundation-model techniques, to Reddit use cases.
• Develop reliable serving and monitoring patterns for low-latency, high-throughput production ML systems.
• Work with cross-functional partners across product, relevance, ads, and core ML teams to deliver measurable improvements in user experience and business impact.
• Drive rigorous offline and online evaluation, including experimentation, model diagnostics, and feedback-loop improvement.
• Contribute to engineering quality through strong code, design reviews, documentation, and operational excellence.

Requirements

• 5+ years of experience in machine learning engineering, with a strong focus on large-scale ML infrastructure and recommendation or personalization systems.
• Expertise in modern deep learning architectures, including sequence models and foundation models.
• Experience building or scaling ML platforms for large datasets and high-traffic production environments.
• Ability to independently scope and execute ambiguous technical work while owning high-quality implementation details.
• Strong understanding of distributed training and inference concepts, including data parallelism, model parallelism, and pipeline parallelism.
• Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow.
• Strong software engineering fundamentals, including system design, debugging, testing, and performance optimization.
• Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems.
• Excellent communication skills, with the ability to present complex ML concepts to technical and non-technical stakeholders.

Compensation and Benefits

• Base salary range: $190,800–$267,100 USD.
• Eligible for equity in the form of restricted stock units; some positions may also be eligible for commission.
• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefits supporting workspace, professional development, caregiving, and family planning.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.