Senior Backend Engineer, ML Systems

This Is An Opportunity To

1. Design, build, and maintain scalable, production-grade machine learning systems for real-time recommendations and generative services.
2. Develop and own the MLOps pipelines for continuous integration, continuous delivery (CI/CD), training, validation, and monitoring of all recommendation models.
3. Engineer robust data pipelines using big data technologies to process vast datasets for model training and feature engineering.
4. Implement and optimize both traditional ML models and state-of-the-art Generative AI models (including LLMs) for low-latency serving and high-throughput environments.
5. Collaborate closely with Applied Researchers to translate novel algorithms and research prototypes into hardened, production-ready code.
6. Develop and manage the APIs and infrastructure necessary for serving recommendations and integrating with other systems.
7. Champion software engineering best practices, including code reviews, testing, and documentation, within the machine learning team.
8. Monitor system performance, identify and resolve production issues, and continuously improve the reliability and efficiency of our ML services.
9. Mentors other team members through code reviews, technical guidance, architecture design, and pair programming.

Qualifications

1. MS in Computer Science or related area with 6 years of relevant work experience (or BS/BA with 8 years) in ML / AI / Data Engineering
2. Expert in production engineering practices and software development in an OO language (Scala, Java, etc.)
3. Extensive experience in big data distributed processing frameworks, e.g. Apache Hadoop, Spark, Flink
4. Experience with ML frameworks like TensorFlow and PyTorch from a production perspective. Experience with serving frameworks (TensorFlow Serving, TorchServe, NVIDIA Triton) and libraries for LLM operations (LangChain, Hugging Face Transformers) preferred.
5. Proven ability to build and manage CI/CD pipelines for ML models, including proficiency with containerization (Docker, Kubernetes).
6. Experience with using cloud services, big data pipelines and databases, e.g. AWS, GCP, Azure
7. Proven ability to design and build scalable, distributed systems and expose their functionality through well-designed RESTful or gRPC APIs
8. A masterful understanding of the challenges and requirements of running machine learning in a live, 24/7 production environment, including monitoring, alerting, and incident response