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.

Responsibilities

• 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.

Qualifications

Core Requirements

• 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/GitHub 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.

Professional Competencies

• 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.

Preferred Qualifications

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