Research Scientist (AI / ML Biologics)
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
Apply Now
Apply Now
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
Apply Now
Apply Now