Staff Machine Learning Infrastructure Engineer at Dyno Therapeutics

Watertown, Massachusetts, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

BiotechnologyIndustries

Requirements

BS with 4+ years of relevant industry experience
Experience with cloud-native infrastructure (GCP or AWS), particularly for ML workflows
Python fluency for scripting, automation, and infrastructure tooling
Experience working in and managing containerized environments (Docker, Kubernetes)
Hands-on experience supporting large-scale ML training and experimentation
Ability to own vendor relationships from a technical perspective
Alignment with Dyno’s core values (step up when things get tough, recalibrate when priorities shift, thrive in high-expectation environment)
Proactive, problem-solving mindset
Preferred qualifications
Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) in production
Experience managing compute budgets and forecasting needs
Prior involvement in cloud infrastructure transitions or system migrations

Responsibilities

Own and optimize ML compute infrastructure: manage dynamic allocation, track usage and costs, and forecast future needs
Partner with other engineers to evolve ML tooling and development environment, improving reproducibility, efficiency, and developer velocity
Deploy ML models into production environments and improve inference performance
Manage vendor relationships (e.g., Google Cloud, Weights & Biases), including technical oversight and future-looking decision making
Work with urgency and adaptability, balancing innovation with execution
Collaborate cross-functionally, leveraging Dyno’s high-trust, high-impact culture to drive results

Skills

Python

GCP

AWS

ML workflows

cloud-native infrastructure

ML compute infrastructure

model deployment

inference performance

Weights & Biases

automation

scripting

Dyno Therapeutics

Develops AI-optimized gene therapy vectors

About Dyno Therapeutics

Dyno Therapeutics focuses on advancing gene therapy by utilizing Artificial Intelligence to create Adeno-associated virus (AAV) vectors. These vectors are essential tools for delivering genetic material into cells, which is crucial for effective gene therapy. The company's AI technology enables the design and optimization of these vectors, potentially enhancing the success of gene therapies. Dyno collaborates with major pharmaceutical and biotech companies, such as Astellas, Roche, Sarepta, and Novartis, to develop therapies for various diseases affecting the skeletal and cardiac muscles, central nervous system, liver, and eyes. Unlike many competitors, Dyno's unique approach leverages AI to improve the performance of AAV vectors, setting it apart in the biotech field. The company's goal is to improve gene therapy outcomes through its advanced vector technology, ultimately benefiting patients with serious health conditions.

Watertown, MassachusettsHeadquarters

2018Year Founded

$106MTotal Funding

SERIES_ACompany Stage

AI & Machine Learning, Biotechnology, HealthcareIndustries

51-200Employees

Benefits

Remote Work Options

Risks

Gene therapy investment slowdown may impact Dyno's growth and innovation.

Manufacturing bottlenecks could hinder scaling of Dyno's operations.

Increased competition from companies like Form Bio challenges Dyno's market position.

Differentiation

Dyno uses AI to design optimized AAV vectors for gene therapy.

Their AI-driven CapsidMap platform enhances AAV vector development for muscle gene therapies.

Partnerships with major pharma companies like Astellas and Roche boost Dyno's market presence.

Upsides

AI-driven capsid design improves delivery efficiency and reduces manufacturing costs.

Collaboration with NVIDIA enhances biological sequence design for gene therapies.

Generative AI increases efficiency of eye and brain-targeted capsid delivery significantly.

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