(Senior) Machine Learning Engineer

Flagship Pioneering, Inc.•Cambridge, MA

10d

About The Position

FL109 is seeking a talented Machine Learning Engineer to support the creation and deployment of state-of-the-art AI solutions to transform how we see molecular biology. You are passionate about solving real-world problems, thrive on shipping impactful solutions, and enjoy working in a dynamic startup environment. Your role will focus on implementing robust ML pipelines, optimizing models for production, and collaborating across teams to turn innovative ideas into scalable products.

Requirements

Hands-on experience with shipping production-grade ML systems, including expertise in deployment, monitoring, and optimization.
Experience with large language models (LLMs), including fine-tuning, inference optimization, and application development.
Proficiency in Python and ML frameworks such as PyTorch.
Strong understanding of software engineering principles, version control (e.g., Git), and CI/CD pipelines.
Familiarity with cloud computing platforms (e.g., AWS) and distributed training of ML models.
Excellent problem-solving skills and the ability to work both independently and collaboratively in a fast-paced environment.
Passion for contributing to impactful projects at the intersection of AI and cutting-edge science.

Nice To Haves

A background in computer vision or experience working with noisy, unstructured data is a plus.

Responsibilities

Pipeline Development: Build and optimize end-to-end ML pipelines, including data preprocessing, model training, validation, and deployment. Ensure solutions are robust, scalable, and efficient.
LLM Integration: Implement and fine-tune large language models for various applications, ensuring their effective integration into existing pipelines and workflows.
Model Deployment: Develop production-grade ML systems that integrate seamlessly with experimental workflows. Focus on creating systems that are both reliable and impactful in real-world applications.
Collaboration: Partner with ML researchers, experimentalists, and other team members to implement solutions aligned with scientific and business objectives.
Code Implementation: Write clean, maintainable, and efficient code to support the design and deployment of AI tools.
Performance Optimization: Implement strategies to improve computational performance and efficiency, including working with cloud-based environments and distributed systems.
Scalability: Work closely with the infrastructure team to ensure AI solutions scale effectively with increasing data and computational demands.