Senior/Staff Machine Learning Engineer

The Role

Senior/Staff Machine Learning Engineer. As a Machine Learning Engineer at Dyno Therapeutics, you will work with Machine Learning researchers to advance modeling efforts and build scalable tools for machine learning and infrastructure. You will play a key role in driving the direction of ML research infrastructure, accelerating research with your expertise, and developing flexible, high-performance systems.

Job Type: Full Time

Location: Watertown, MA or NYC (may consider remote candidates)

How You Will Contribute

As a Senior/Staff Machine Learning Engineer, you will accelerate research by collaborating on modeling efforts, building tools and data pipelines that improve the team’s efficiency, and driving the future direction of the ML infrastructure at Dyno. 

Responsibilities: 

• Collaborate with ML scientists and engineers to understand their needs and translate them into effective tools and systems that accelerate ML research.
• Manage ML infrastructure, including configuring and maintaining GPU compute resources.
• Design, develop, and maintain scalable data pipelines for ML data from external (PDB, UniProt, etc.) and internal sources.
• Develop, deploy, and monitor deep learning models built primarily using PyTorch but potentially other frameworks (e.g. Jax).
• Work with SQL and NoSQL databases for efficient data retrieval, storage, and analysis.
• Conduct unit testing, code reviews, and follow software design principles to ensure high-quality code.
• Stay current with emerging ML techniques, tools, and trends, and apply them to improve systems and workflows.

Basic qualifications

• 3+ years of post-graduate, full time work experience in industry.
• Strong foundation in software engineering with proficiency in programming languages such as Python.
• Experience building data systems to support machine learning, including data processing libraries such as Pandas and NumPy.
• Proficiency in SQL and NoSQL databases for data retrieval and storage.
• Experience deploying and developing deep learning models using frameworks like PyTorch, Jax, Keras, or Tensorflow.
• Familiarity with MLOps systems for training, monitoring, and evaluating ML models.
• Experience building cloud-native systems and infrastructure, with an understanding of the benefits of GPU compute.
• Alignment with Dyno’s core values.

Preferred qualifications

• Experience handling large datasets using platforms like Apache Spark or Hadoop.
• Experience managing GPU compute infrastructure.
• Previous experience in a machine learning and biology-focused company.