AI/ML Engineer

We've partnered with a well-funded, fast-growing startup in the biotech space to help them find AI/ML Engineers. Their product helps pharma, biotech, and investors figure out whether a drug or biotech asset is worth acquiring, licensing, investing in, or developing. They're headquartered in Cambridge, MA with offices in SF and NYC, and looking for candidates who can come into one of the offices twice a week, while working remotely three days a week.

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.

What you'll bring:

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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.