Company Overview: Abiologics, Inc. is a privately held, early-stage biotechnology company on a mission to make biology better through chemistry. We are pioneering the development of Synteins^TM, a transformational class of macromolecular medicines unlocked by novel synthetic and computational technologies. Abiologics is actively seeking exceptional molecular scientists who are passionate about driving chemical innovation in biotechnology.

Abiologics was founded in Flagship Pioneering’s venture creation engine, where companies such as Moderna Therapeutics (NASDAQ: MRNA) and Generate Biomedicines were conceived and created. Since Flagship’s founding in 2000, the firm has originated and fostered the development of more than 100 scientific ventures resulting in more than 500 issued patents and more than 50 clinical trials for novel therapeutic agents.

Position Summary: As part of the Abiologics team, Senior MLOps Engineers are responsible for developing the frameworks which provide the underlying software engineer for our advanced models, training scaling, inference scaling, and model governance. Successful candidates will collaborate closely with expert machine learning engineers, protein designers, and the software infrastructure team to deliver impactful models that enhance our drug discovery pipelines.

Responsibilities:

• Design and implement scalable distributed computing solutions tailored for advanced machine learning workflows, ensuring seamless integration and optimization of ML operations.
• Collaborate with cross-functional teams to streamline and enhance ML operations, leveraging distributed computing frameworks to drive efficiency and performance.
• Develop and maintain robust infrastructure for deploying, monitoring, and managing machine learning models in production environments, focusing on reliability and scalability.
• Lead the design and implementation of automated workflows and pipelines for data processing, model training, and deployment, optimizing the end-to-end ML lifecycle.

Qualifications:

• A masters or PhD in computational chemistry, computer science, or a related discipline solving biological or chemical problems using computational approaches.
• Proven experience in distributed computing, parallel processing, and cluster management, with a strong background in building and optimizing workflows for machine learning applications.
• Proficiency in ML Ops practices, including model deployment, monitoring, and version control, with expertise in tools like Kubernetes, Docker, and CI/CD pipelines.
• Solid programming skills in Python coupled with a deep understanding of cloud computing platforms and distributed systems.

Preferred:

• 4+ years of industry experience in software engineering or ML operations
• Excellent collaboration skills, with the ability to think independently and contribute to a dynamic intellectual environment.
• Expertise with common software development tools and best practices: Git, cloud/cluster computing, testing frameworks, etc.
• Experience with workflow tools such as Dask, Prefect, MLFlow, etc.
• Experience with IaC tools such as Terraform, CloudFormation, etc.
• Demonstrated experience in the Python programming language in addition to standard machine learning tools (PyTorch, PyG, PyMC, etc).