Associate Principal Scientist, Senior Machine Learning Engineer

Build and own the team’s ML lifecycle tooling, including reproducible training and fine‑tuning pipelines, experiment tracking, model registries, and the packaging and deployment of deep learning models. Drive efficient and reproducible model development by managing compute environments, GPU workloads, and large‑scale biomedical datasets on cloud platforms such as AWS/SageMaker and Databricks. Transform prototype code into robust, reusable solutions, establishing frameworks, templates, and best practices that accelerate delivery across projects. Collaborate closely with data engineering, platform, and AI science teams to integrate ML solutions into the broader Computational Innovation ecosystem. Evolve from ML platform ownership toward applied ML/DL development, contributing increasingly to model adaptation, fine‑tuning, evaluation, and the delivery of AI solutions for scientific use cases as the platform matures. Requirements
Degree in Computer Science, Engineering, or a related field, with hands‑on experience as an ML Engineer, MLOps Engineer, or in a similar role. Strong software engineering skills in Python, with proven experience deploying, serving, and maintaining ML/DL models in production environments. Demonstrated MLOps expertise, including reproducible pipelines, experiment tracking, model versioning and registries, containerization (Docker), and cloud‑based ML infrastructure (AWS, Azure, or similar platforms). Strong interest and aptitude in Machine Learning and Deep Learning, with hands‑on experience training and fine‑tuning models and a desire to grow further in applied ML/DL development. Experience or interest in scientific, pharmaceutical, or biomedical applications is a strong advantage, although deep domain expertise is provided by other members of the team. Core Competencies
Demonstrates expertise in Machine Learning and Deep Learning, with a strong foundation in Python programming and MLOps practices. Capable of building and managing ML lifecycle tooling, deploying models in production, and collaborating across teams to deliver AI solutions for scientific applications. Highest-signal resume keywords
Machine Learning Lifecycle Tooling MLOps Expertise Python Programming Cloud-Based ML Infrastructure Model Deployment and Maintenance ATS Optimization Keywords
Hard Skills
Machine Learning Deep Learning MLOps Experiment Tracking Model Versioning Containerization Reproducible Pipelines Model Registries Fine-Tuning Models Data Engineering Industry Keywords
Biomedical Applications Pharmaceutical Applications Computational Innovation Cloud Platforms Tools & Technologies
AWS SageMaker Databricks Docker Azure
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