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Posted on:11/09/2026
20
Similar Jobs
Depends on experience
Agentic AI,Deep Learning,Generative AI,JAX,Machine Learning Frameworks,Nucleic,Oligonucleotide,Peptide,Python,PyTorch,R and SQL,RNA,Scikit-Learn,Tensor Flow
Work type: Remote
Work authorization: US local resources,Work authorization required,Work authorization sponsored,Work authorization in field of study
Duration: 6 months
Veteran Service: Yes
Number of hirings: 1
Job Details
AI/ML Research Scientist
Job Details
- Location: Remote (100% Remote)
- Duration: 6 Months
- Engagement Type: C2C
- Visa Restrictions: US Citizens (USC) and Green Card (GC) Holders Only
- Interview Process: Video Interview
Job Summary
We are seeking a highly skilled Computational Biology / AI-ML Scientist with expertise in computational biology, machine learning, and generative AI-driven therapeutic design. The ideal candidate will have hands-on experience developing deep learning, generative, or agentic AI models for nucleic acid and/or protein design, particularly in RNA, oligonucleotide, peptide, or antibody design.
The candidate will develop predictive models to optimize oligonucleotide therapeutics, including exon-skipping efficiency and ASO/siRNA potency, while contributing to AI-driven de novo design and optimization of antibodies, peptides, and other biologics. This role requires strong computational modeling skills and the ability to collaborate with multidisciplinary teams to translate computational predictions into experimentally validated designs.
Required Qualifications (Must Have)
- PhD in Computational Biology, Computational Chemistry, Machine Learning, Bioengineering, Chemical Engineering, or a related field, with 1–2 years of relevant industry or postdoctoral experience.
- Strong knowledge of at least one of the following domains: RNA, Oligonucleotide, Peptide, or Antibody Design.
- Hands-on experience developing deep learning, generative AI, or agentic AI models for nucleic acid and/or protein design.
- Proficiency in Python and modern machine learning frameworks, including PyTorch, JAX, TensorFlow, or scikit-learn.
- Working knowledge of R and SQL.
- Strong understanding of oligonucleotide design principles, including:
- Hybridization thermodynamics
- Target accessibility
- RNA secondary structure
- Experience modeling exon-skipping efficiency or ASO/siRNA potency using sequence information and pre-mRNA structural features.
- Experience with protein and RNA language models, such as ESM and RNA-FM.
- Familiarity with protein/RNA structure prediction tools, including AlphaFold and RoseTTAFold.
- Experience with molecular modeling platforms such as Schrödinger or MOE.
- Exposure to computational modeling of antibody–antigen sequence, structure, and binding interactions.
- Familiarity with uncertainty quantification and active learning techniques for experimental design.
- Experience with reproducible software development and deployment practices, including:
- Git (GitHub/GitLab)
- Docker
- Database systems
- Cloud computing
- Excellent communication and collaboration skills, with the ability to work effectively across multidisciplinary teams of chemists, biologists, and data scientists.
Nice-to-Have Qualifications
- Experience with AWS cloud computing and related services.
Key Responsibilities
- Develop sequence-aware predictive models to prioritize oligonucleotide therapeutics based on exon-skipping response.
- Integrate sequence-based, thermodynamic, and pre-mRNA structural features into predictive modeling workflows.
- Extend computational modeling approaches across multiple therapeutic modalities, including:
- PMOs (Phosphorodiamidate Morpholino Oligomers)
- Gapmers
- siRNA–PMO hybrids
- Conjugate designs
- Contribute to AI/ML-driven de novo design and multi-objective optimization of antibodies, peptides, and other biologics.
- Develop and apply computational models for protein structure prediction and antibody–antigen interactions.
- Collaborate with chemists, biologists, and data scientists to benchmark and prospectively validate model predictions.
- Translate computational insights into experimentally testable therapeutic designs.
- Expand cross-modality AI-driven design capabilities and computational decision-support systems.
- Apply uncertainty quantification and active learning to improve model performance and guide experimental prioritization.
Ideal Candidate Profile
The ideal candidate will combine a strong academic foundation in computational biology or a related discipline with practical experience in AI/ML-driven molecular design. Candidates with expertise in oligonucleotide therapeutics, RNA/protein language models, exon-skipping prediction, and generative AI-based biologics design will be particularly relevant.
Job Details
- Location: Remote (100% Remote)
- Duration: 6 Months
- Engagement Type: C2C
- Visa Restrictions: US Citizens (USC) and Green Card (GC) Holders Only
- Interview Process: Video Interview
Job Summary
We are seeking a highly skilled Computational Biology / AI-ML Scientist with expertise in computational biology, machine learning, and generative AI-driven therapeutic design. The ideal candidate will have hands-on experience developing deep learning, generative, or agentic AI models for nucleic acid and/or protein design, particularly in RNA, oligonucleotide, peptide, or antibody design.
The candidate will develop predictive models to optimize oligonucleotide therapeutics, including exon-skipping efficiency and ASO/siRNA potency, while contributing to AI-driven de novo design and optimization of antibodies, peptides, and other biologics. This role requires strong computational modeling skills and the ability to collaborate with multidisciplinary teams to translate computational predictions into experimentally validated designs.
Required Qualifications (Must Have)
- PhD in Computational Biology, Computational Chemistry, Machine Learning, Bioengineering, Chemical Engineering, or a related field, with 1–2 years of relevant industry or postdoctoral experience.
- Strong knowledge of at least one of the following domains: RNA, Oligonucleotide, Peptide, or Antibody Design.
- Hands-on experience developing deep learning, generative AI, or agentic AI models for nucleic acid and/or protein design.
- Proficiency in Python and modern machine learning frameworks, including PyTorch, JAX, TensorFlow, or scikit-learn.
- Working knowledge of R and SQL.
- Strong understanding of oligonucleotide design principles, including:
- Hybridization thermodynamics
- Target accessibility
- RNA secondary structure
- Experience modeling exon-skipping efficiency or ASO/siRNA potency using sequence information and pre-mRNA structural features.
- Experience with protein and RNA language models, such as ESM and RNA-FM.
- Familiarity with protein/RNA structure prediction tools, including AlphaFold and RoseTTAFold.
- Experience with molecular modeling platforms such as Schrödinger or MOE.
- Exposure to computational modeling of antibody–antigen sequence, structure, and binding interactions.
- Familiarity with uncertainty quantification and active learning techniques for experimental design.
- Experience with reproducible software development and deployment practices, including:
- Git (GitHub/GitLab)
- Docker
- Database systems
- Cloud computing
- Excellent communication and collaboration skills, with the ability to work effectively across multidisciplinary teams of chemists, biologists, and data scientists.
Nice-to-Have Qualifications
- Experience with AWS cloud computing and related services.
Key Responsibilities
- Develop sequence-aware predictive models to prioritize oligonucleotide therapeutics based on exon-skipping response.
- Integrate sequence-based, thermodynamic, and pre-mRNA structural features into predictive modeling workflows.
- Extend computational modeling approaches across multiple therapeutic modalities, including:
- PMOs (Phosphorodiamidate Morpholino Oligomers)
- Gapmers
- siRNA–PMO hybrids
- Conjugate designs
- Contribute to AI/ML-driven de novo design and multi-objective optimization of antibodies, peptides, and other biologics.
- Develop and apply computational models for protein structure prediction and antibody–antigen interactions.
- Collaborate with chemists, biologists, and data scientists to benchmark and prospectively validate model predictions.
- Translate computational insights into experimentally testable therapeutic designs.
- Expand cross-modality AI-driven design capabilities and computational decision-support systems.
- Apply uncertainty quantification and active learning to improve model performance and guide experimental prioritization.
Ideal Candidate Profile
The ideal candidate will combine a strong academic foundation in computational biology or a related discipline with practical experience in AI/ML-driven molecular design. Candidates with expertise in oligonucleotide therapeutics, RNA/protein language models, exon-skipping prediction, and generative AI-based biologics design will be particularly relevant.
Interview type: Telephonic
Experience years required: 0