Job Description

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.

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