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Connecticut Innovations · 1 month ago

Deep Learning Scientist

New Haven, CT

Full-time

Onsite

Senior Level

Connecticut Innovations is Connecticut’s strategic venture capital arm, passionate about serving its portfolio of companies. The Deep Learning Scientist will design, develop, and deploy advanced deep learning models for analyzing biological data, while collaborating with cross-functional teams to integrate datasets and optimize models for large-scale applications.

BiotechnologyEnergyInformation TechnologyMedia and Entertainment

H1B Sponsor Likely

Hiring Manager

Marina D.

Responsibilities

Design, develop, and deploy state-of-the-art deep learning models for analyzing multi-modal biological data

Collaborate with bioinformatics, experimental biology and engineering teams to integrate multi-modal human biology datasets into cohesive AI frameworks

Develop deep learning architectures that incorporate biological inductive biases, and explore generative graph representation learning to reveal novel patterns in brain data

Optimize deep learning pipelines for petabyte-scale datasets and ensure models are scalable on high-performance computing infrastructures

Rigorously validate model outputs against biological benchmarks and iterate based on experimental feedback

Publish research findings and present at scientific conferences to contribute to the broader AI and biomedical communities

Keep abreast of the latest advancements in deep learning and AI, ensuring our models leverage cutting-edge innovations

Qualification

Deep LearningGraph Neural NetworksPyTorchHigh-Performance ComputingBiological Data IntegrationModel DeploymentCross-Disciplinary CollaborationCommunication SkillsProblem-Solving Skills

Required

PhD in Computer Science, Machine Learning, or alternative STEM field (e.g., biology or physics) with appropriate experience

Demonstrated track record of applying deep learning to biological problems (e.g., genomics, transcriptomics, proteomics, or imaging)

Graph & Geometric Deep Learning: Strong practical experience with geometric deep learning and graph neural networks (GNNs); proven ability to tailor these methods to biological data, especially transcriptomics

Expertise in PyTorch with the ability to build and deploy scalable models

Experience integrating diverse data types (e.g., transcriptomics, proteomics, mass spectroscopy, etc) using deep learning

Familiarity with developing production-quality pipelines, cloud computing, and model deployment best practices

Excellent communication skills and the ability to work cross-functionally with engineers, biologists and other key stakeholders to convert raw data output into neuroscience discoveries

Demonstrated ability to research and implement novel deep learning architectures tailored to complex biological datasets

Strong problem-solving skills and ability to work effectively in a cross-disciplinary team (collaborating with bioinformaticians, neuroscientists, experimentalists and engineers)

HPC & Distributed Training: Experience with high-performance computing (HPC) environments or distributed training techniques for large-scale GNN models

Communication Skills: Excellent communication skills for presenting findings and collaborating effectively with diverse stakeholders

Preferred

Experience with graph neural networks and generative graph representation learning

Background or collaborative experience in biological sciences or neuroscience

Prior experience integrating AI models with high-fidelity biological data

A publication track record in leading AI/ML conferences or in computational biology/neuroscience journals

Benefits

Strict wage minimums

Generous benefits

Personal leave policies

Company

Connecticut Innovations

Connecticut Innovations provides strategic capital and operational insight to to companies in high-tech industries

Founded in 1995

Rocky Hill, Connecticut, USA

11-50 employees

http://www.ctinnovations.com

H1B Sponsorship

Connecticut Innovations has a track record of offering H1B sponsorships. Please note that this does not guarantee sponsorship for this specific role. Below presents additional info for your reference. (Data Powered by US Department of Labor)

Distribution of Different Job Fields Receiving Sponsorship

Represents job field similar to this job

Trends of Total Sponsorships

2025 (1)

2021 (1)

2020 (1)