Principal Software Engineer, Machine Learning Deployment

Working at SambaNova

Do you want to build the next generation of Machine Learning and Artificial Intelligence? Would you like to be part of a world-class team of scientists and engineers at a rapidly growing startup? If your answer is ‘yes’, join SambaNova Systems and help us build and deploy models and systems on a scale never imagined before.

As a Machine Learning Deployment Engineer, you will help develop AI models and deploy them at scale into various SambaNova platforms. You will be responsible for understanding how the ML engineers and other customers interact with the models, including but not limited to data transformations, APIs, and user interfaces. You will build infrastructure for complete model delivery to the customer on SambaNova’s platforms.

Responsibilities

• Deploy machine learning applications on SambaNova platforms
• Build infrastructure for end-to-end deployment for the machine learning models and applications on SambaNova systems
• Measure, validate, and optimize the statistical performance of ML models on SambaNova platforms
• Understand and implement data pipelines and transformations for various popular models (eg. Llama 3)
• Deploy ML models across public and private clouds including container-based Kubernetes environments
• Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems
• Build both batch and real-time model prediction pipelines with existing application and front-end integrations

Required Qualifications

• BS plus 2-8 years of industry experience in a highly quantitative field (Computer Science, Machine Learning, Informatics, Mathematics, etc.)
• 2+ years of experience of building and deploying Machine Learning models
• 4+ years of experience programming in C++, Python, or related technologies
• 2+ years of experience with one or more deep learning frameworks, such as TensorFlow, PyTorch, MXNet, or Caffe2
• Knowledge and understanding of machine learning
• Understanding of data pipelines in popular machine learning models such as LLMs
• Strong understanding of data structures and algorithms
• Strong analytical and debugging skills
• Interest in high-performance systems engineering and performance debugging
• Comfortable working in a fast-paced, highly collaborative environment

Preferred Qualifications

• Experience building and training production-grade ML models on large-scale datasets to solve various business use case
• Experience with large-scale data processing frameworks such as Spark and AWS EMR for feature engineering and be proficient across both structured and unstructured data
• Experience with LLMs for solving various business use cases such as entity resolution, forecasting, and anomaly detection
• Experience deploying applications in cloud infrastructure such as AWS, GCP, and Azure
• Familiarity with cloud-based ML offerings
• Experience building hybrid cloud infrastructure

Annual Salary Range and Level

The base salary for this position ranges from $165,000/year up to $195,000/year. This range is based on role, level, and location and reflects the salary target for new hires in the US. Individual pay within the range will depend on a number of factors, including a candidate’s job-related qualifications, skills, competencies and experience, and location.

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