Role Overview
This role supports the advancement of antisense oligonucleotide (ASO) therapeutics and biologics discovery through the integration of advanced machine learning (ML), artificial intelligence (AI), and data-driven modeling. The Research Scientist will develop reproducible computational frameworks to enhance AI/ML-driven design pipelines across multiple therapeutic modalities, including oligonucleotides and biologics.
Key Responsibilities
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Design and implement advanced AI/ML approaches for de novo antibody discovery, including fine-tuning protein language models and developing generative protein design workflows
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Develop and scale machine learning models to support multi-objective optimization of antibodies, antigens, ADCs, and other biologic modalities
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Build and extend sequence-aware predictive models to prioritize ASO designs based on exon-skipping response across diverse modalities (e.g., PMO gapmers, siRNA hybrids, conjugates)
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Establish end-to-end computational frameworks, including data ingestion, feature engineering, model development, validation, and deployment
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Curate and integrate internal and external datasets; define key sequence and structural features (e.g., thermodynamics, accessibility, motifs, secondary structure) to drive model performance
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Develop benchmarking strategies and prospective validation approaches in collaboration with experimental teams
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Evaluate and implement proprietary and open-source tools to enhance modeling workflows and decision-making
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Maintain well-documented, scalable codebases and provide user guidance for cross-functional stakeholders
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Support additional related initiatives as needed
Required Qualifications
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PhD in Computational Biology, Computational Chemistry, Machine Learning, Biomedical Engineering, Chemical Engineering, or related field, with 3+ years of relevant industry experience
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Strong background in oligonucleotide chemistry and/or antibody design and characterization
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Proven experience in computational modeling of antibody-antigen interactions (sequence, structure, binding)
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Expertise in machine learning methodologies, including deep learning and probabilistic models (e.g., RNNs, GNNs, Transformers, NLP models, Generative AI)
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Proficiency in programming languages such as Python, R, and SQL, with hands-on experience using frameworks like PyTorch, TensorFlow, scikit-learn, or JAX
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Experience developing ML models for DNA, RNA, and protein applications, including sequence modeling and structure prediction
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Familiarity with cloud computing environments, large-scale data processing, and production-level development tools
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Experience with tools and infrastructure such as AWS, databases, GitHub/GitLab, and Docker
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Strong communication and collaboration skills with cross-functional teams (e.g., biology, chemistry, data science)
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Demonstrated ability to work independently and contribute effectively in a team environment
Impact
Full-time employees are also eligible for benefits options such as health coverage, life insurance, disability insurance, and 401k benefits.
At Advanced Group, our commitment to diversity and inclusion in every part of our organization is crucial to fulfilling our mission and demonstrating our REAL values. Advanced Group is committed to providing employment opportunities without regard to sex, race, color, age, national origin, religion, gender identity or expression, sexual orientation or sexual preference, pregnancy or maternity, genetic information, marital status, disability, veteran status, or any other basis protected by applicable federal, state or local law.
Advanced Group complies with federal and state disability laws and makes reasonable accommodations for applicants and candidates with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please contact accommodationrequest@advancedgroup.com.
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