We’re looking for a driven, hands-on ML/AI Engineer to help us push the boundaries of what's possible in AI-driven drug development. You’ll work on production-grade LLM-based systems, knowledge graphs, and machine learning pipelines—turning prototypes into powerful tools that make a real-world impact.

You'll collaborate across teams to design, build, and deploy intelligent systems that enhance how life sciences organizations make critical decisions. If you love working on technically challenging problems with direct impact in healthcare—this is the role for you.

This is a remote-first position, but we’d love it if you’re based in or near Boston.

What You'll Do:

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  Design and Deploy LLM Systems:
  Develop scalable, production-ready LLM applications using frameworks like LangChain/LangGraph. Build robust RAG pipelines and integrate knowledge graphs for biological and clinical data.
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  Full-Stack AI Engineering:
  Write maintainable, high-performance code and build clean APIs and services for machine learning applications.
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  Data Engineering Collaboration:
  Work with data engineers to build and optimize data workflows and pipelines for high-quality data ingestion and processing.
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  Product-Focused Prototyping:
  Collaborate with product and domain teams to rapidly prototype AI solutions, iterate based on feedback, and scale models for production.
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  Model Deployment & MLOps:
  Use modern MLOps tools to deploy and monitor models in production environments (AWS preferred). Ensure scalability, observability, and resilience.
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  Collaborative Innovation:
  Partner with engineering, data, and business teams to identify and develop high-value AI/ML applications.
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  Continuous Learning:
  Stay ahead of the curve on emerging ML frameworks, GenAI capabilities, and healthcare technologies.

Requirements

Core Qualifications:

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  Education:
  Bachelor's, Master’s, or Ph.D. in Computer Science, Data Science, Engineering, or a related field.
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  Hands-on AI Experience:
  Proven ability to build, train, and deploy ML and NLP models, especially those powered by LLMs and transformer architectures.
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  LLM & LangChain Experience
  : Practical experience working with frameworks like LangChain for applications such as Q&A systems, chatbots, or document automation.
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  Software Engineering:
  Strong coding skills in Python and experience using Git/GitHub and CI/CD practices.
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  Data Engineering Know-how:
  Comfort working with ETL pipelines, relational and non-relational databases, and data platforms like Snowflake or Databricks.
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  Big Data & ML Frameworks:
  Familiarity with Big Data tools (e.g., Apache Spark) and experience orchestrating data workflows using tools like Apache Airflow.
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  Cloud & MLOps:
  Experience with deploying ML models in cloud environments (AWS, GCP, or Azure) and using containerization/orchestration tools like Docker and Kubernetes.

Soft Skills:

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  Strong problem-solving skills and an analytical mindset.
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  Passion for continuous learning, rapid prototyping, and iterating based on user needs.
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  Autonomous, self-starter attitude with a strong sense of ownership.
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  Excellent communication skills—able to explain technical ideas clearly to non-technical audiences.
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  Collaborative team player with a desire to build things that truly matter.