Want to become an AAViator?

The Role Machine Learning Research Scientist. This position is ideal for a researcher who is enthusiastic about applying cutting-edge machine learning to solve high-impact problems in gene therapy. Job Type: Full-time Location: Watertown, MA How You Will Contribute As a Machine Learning Research Scientist, you will be a critical member of Dyno’s world-class machine learning team. You will leverage your expertise to design, implement, and evaluate ML models to solve complex biological sequence modeling problems. Your work will directly advance Dyno’s mission building high performance genetic technologies to transform patient lives. Responsibilities: Lead the design, development, and evaluation of machine learning models to optimize protein and gene delivery performance. Collaborate closely with computational biologists, experimentalists, and software engineers to integrate ML solutions into Dyno’s core platform. Analyze and derive insights from one of the most unique datasets of in-vivo measured proteins in the world. Drive projects end-to-end, from research and development to deployment, with high-quality code contributions to Dyno’s codebase. Communicate results and findings successfully to internal stakeholders and the broader scientific community. Who you are A collaborative team player with well-developed scientific and technical expertise. Detail-oriented, with a commitment to scientific rigor and reproducibility. Driven by a sense of urgency and an affinity for impact. A creative problem solver who thrives in a dynamic, interdisciplinary environment. Basic qualifications PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field. Deep programming skills, with proficiency in Python. Skilled in designing, implementing, and evaluating machine learning models, including expertise with PyTorch or equivalent frameworks. Demonstrated ability to inspect and draw clear insights from large datasets. Proficient communication skills, with the ability to present technical results to diverse audiences. Preferred qualifications Prior experience applying machine learning to biological sequence design. Industry experience in machine learning research and model deployment. Publications in leading ML or ML-biology venues.

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.