Senior AI​/ML Engineer

Job Title:

Senior ML/AI Engineer Position Overview

We are seeking a proactive, hands‑on Senior
ML/AI Engineer to help us advance the frontier of intelligent systems within the sector of advanced therapeutic research. In this role, you will develop production‑grade LLM‑based systems, manage knowledge graphs, and architect machine learning pipelines—transforming initial prototypes into robust tools that deliver significant real‑world impact.

As a key member of our technical team, you will collaborate cross‑functionally to design, build, and deploy intelligent frameworks that improve how organizations in the life sciences space navigate critical decision‑making processes. If you are passionate about solving complex technical challenges that directly influence the future of healthcare technology, this role offers an ideal opportunity.

Location: This is a HYBRID position targeting candidates based in or near the Boston or San Francisco areas.

• Design and Deploy LLM Systems: Develop scalable, production‑ready LLM applications utilizing industry‑standard frameworks. Architect robust Retrieval‑Augmented Generation (RAG) pipelines and integrate knowledge graphs for complex biological and clinical datasets.
• Full‑Stack AI Engineering: Author maintainable, high‑performance code and develop clean APIs and services to support machine learning applications.
• Data Engineering
  
  Collaboration:
  
  Partner with data engineering teams to construct and optimize data workflows for high‑quality ingestion and processing.
• Product‑Focused Prototyping: Engage with product and domain experts to rapidly prototype AI‑driven solutions, iterating based on stakeholder feedback to scale models for production environments.
• Model Deployment & MLOps: Utilize modern MLOps tools to deploy and monitor models in production (with a preference for AWS environments). Ensure systems are built for scalability, observability, and resilience.
• Collaborative Innovation: Work alongside engineering, data science, and business leadership to identify and execute on high‑value AI/ML initiatives.
• Continuous Learning: Maintain a deep understanding of emerging ML frameworks, generative AI capabilities, and relevant healthcare technologies.

• Education: Bachelors, Masters, or Ph.D. in Computer Science, Data Science, Engineering, or a closely related technical field.
• Hands‑on AI
  
  Experience:
  
  A proven track record of building, training, and deploying ML and NLP models, specifically those utilizing Large Language Models (LLMs) and transformer architectures.
• Framework Proficiency: Practical experience leveraging specialized frameworks for applications such as Q&A systems, automated chatbots, or document processing automation.
• Software Engineering: Advanced proficiency in Python, with a strong grasp of Git/Git Hub version control and CI/CD best practices.
• Data Engineering Knowledge: Professional comfort managing ETL pipelines, relational/non‑relational databases, and enterprise data platforms.
• Big Data & ML Frameworks: Familiarity with Big Data tools (e.g., Apache Spark) and experience orchestrating complex data workflows with tools such as Apache Airflow.
• Cloud & MLOps: Experience deploying ML models within cloud environments (AWS, GCP, or Azure) using containerization and orchestration tools like Docker and Kubernetes.

• Strong analytical mindset paired with exceptional problem‑solving skills.
• A passion for continuous professional development and rapid, iterative prototyping.
• An autonomous, self‑starting approach with a clear sense of project ownership.
• Superior communication skills, with the ability to translate technical concepts for non‑technical stakeholders.
• A collaborative spirit and a drive to build tools with meaningful impact.

• Prior experience within the healthcare, life sciences, or biopharmaceutical industries is an asset but not strictly required.