Senior Machine Learning Infrastructure Engineer, Embedding Platform
$190,800–$267,100 year
RemoteUnited States
Job Summary
Design, train, and improve large-scale machine learning platforms for recommendation or personalization systems by owning major 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 while enhancing distributed training, model efficiency, and online inference performance. Apply modern modeling approaches including sequence modeling and foundation-model techniques to Reddit use cases, developing reliable serving and monitoring patterns for low-latency, high-throughput production systems. Drive rigorous offline and online evaluation, including experimentation, model diagnostics, and feedback-loop improvement, collaborating with cross-functional partners across product, relevance, ads, and core ML teams.
Required Qualifications
- 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 foundational models
- Experience building or scaling ML platform for large datasets and high-traffic production environments
- Demonstrated ability to independently scope and execute ambiguous technical work, while owning high-quality implementation details
- Solid understanding of distributed training and inference concepts, such as data parallelism, model parallelism, pipeline parallelism, or related optimization techniques
- Proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar
- 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 effectively present complex ML concepts to technical and non-technical stakeholders
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