Research Scientist (AI /ML Biologics)

About the Role

We are seeking a highly motivated Research Scientist to support cutting-edge work at the intersection of AI, machine learning, and biologics discovery. This role focuses on building scalable, data-driven modeling frameworks to accelerate therapeutic design across oligonucleotides and biologic modalities. You will play a key role in advancing AI/ML-assisted discovery pipelines, helping drive innovation in sequence-based modeling, antibody design, and next-generation therapeutics.

Location: Open to Remote, within 1 hour of the Cambridge, MA 02142.

What You’ll Do

• Develop and implement advanced AI/ML models for antibody discovery, including generative protein design and protein language models
• Build and scale machine learning approaches for multi-objective optimization across biologic modalities
• Design sequence-aware predictive models to support oligonucleotide therapeutic development, including exon skipping response
• Create end-to-end computational frameworks covering data ingestion, feature engineering, model training, validation, and deployment
• Curate and integrate diverse datasets, including literature-based and experimental data
• Define and engineer key biological features such as sequence motifs, thermodynamics, and structural attributes
• Establish model benchmarks and collaborate with experimental teams to validate predictions
• Evaluate and integrate new tools and technologies to enhance modeling workflows
• Maintain clean, well-documented codebases and provide guidance to cross-functional teams

What We’re Looking For

• PhD in Computational Biology, Computational Chemistry, Machine Learning, Biomedical Engineering, or a related field
• 3+ years of relevant experience in industry or highly applicable post-PhD academic research
• Strong background in oligonucleotide chemistry and/or antibody design and characterization
• Experience modeling antibody-antigen interactions, including sequence and structural analysis
• Hands-on expertise with machine learning and deep learning methods such as RNNs, GNNs, Transformers, and generative models
• Proficiency in Python, R, and SQL, along with frameworks like PyTorch, TensorFlow, scikit-learn, or JAX
• Experience working with DNA, RNA, and protein modeling, including structure prediction and design
• Familiarity with cloud platforms, large-scale computing, and data infrastructure tools such as AWS, Docker, GitHub, or GitLab
• Strong communication skills and ability to collaborate across multidisciplinary teams

Nice to Have

• Experience working in cross-modality therapeutic design (e.g., biologics and oligonucleotides)
• Exposure to production-level ML systems and scalable pipelines

Why Join

• Work on impactful, next-generation therapeutic technologies
• Collaborate with a highly interdisciplinary team of scientists and engineers
• Opportunity to contribute to innovative AI-driven drug discovery programs
• Flexible consideration for strong candidates from academic backgrounds

Interested? Apply now to learn more.