Senior Machine Learning Engineer

You Thrive Here By Possessing the Following:

• 5+ years of experience in building and deploying production-grade machine learning systems.
• Strong Python and SQL skills, with familiarity in containerization tools like Docker, and an understanding of secure model access via APIs.
• Familiarity with AWS cloud services, including SageMaker, Amazon Bedrock, and Amazon Q.
• Experience automating MLOps pipelines and deploying models for both batch and real-time inference.
• Hands-on expertise in developing GenAI proofs-of-concept and taking them through to production, as well as experience scaling traditional ML models for production at scale.
• A solid understanding of AI explainability, including experience with model interpretability techniques, observability, and monitoring to ensure performance and reliability.
• Familiarity with applying FinOps concepts to optimize cloud resources, reduce costs, and ensure the efficient scaling of AI models.
• An appreciation for both the opportunities and challenges associated with Generative AI, including the ability to identify risks, manage ethical considerations, and maintain a balanced approach to applying new technologies.
• A collaborative mindset, with strong communication skills and a passion for mentoring and growing the capabilities of the ML team.

Nice to Haves:

• Experience with parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA).
• Understanding of modern deep learning architectures, including transformers and Mamba.
• Experience publishing or presenting at leading conferences, including NeurIPS, ICML, ICLR, AAAI, or ACL.