Senior Machine Learning Engineer

What you'll do

• Design and operate infrastructure to host and serve classical and deep learning models with a focus on low latency and scalability.
• Evaluate and select tools, frameworks, and infrastructure to balance trade-offs between cost, performance, and reliability.
• Build scalable data pipelines for training data collection, human annotation, and LLM-based labeling.
• Implement monitoring, observability, and alerting systems to ensure production ML systems meet performance SLAs.
• Develop shared codebases and common data processing libraries to optimize machine learning workloads.
• Analyze user interaction data to identify gaps in training distributions and define model success metrics.
• Build infrastructure for embedding pipelines, including vector store design and retrieval strategies.
• Implement inference optimization techniques such as quantization, batching, and model distillation.

What we're looking for

• MS in Computer Science, Machine Learning, or a related discipline, or equivalent work experience.
• 5+ years of industry experience in machine learning infrastructure or software engineering with a strong ML systems focus.
• Strong proficiency in Java and Python for production serving systems.
• Hands-on experience building and shipping production ML infrastructure including model serving, deployment pipelines, and feature delivery.
• Experience deploying ML models on cloud platforms like AWS and/or GCP while managing trade-offs in latency, cost, and scalability.
• Experience with RAG architectures and deploying agentic AI systems in production.
• Experience building data pipelines for A/B test analysis and training dataset creation using tools such as Apache Spark.
• Strong cross-functional communication skills to translate complex technical concepts for non-technical partners.