BayOne Solutions · 4 hours ago
Artificial Intelligence Researcher
BayOne Solutions is seeking a highly motivated Junior/Associate AI Scientist to develop machine learning methods for research biology and target discovery. The role involves applying modern sequence-based machine learning techniques to improve variant effect prediction and collaborating with a cross-functional team of mentors and scientists.
Responsibilities
Develop and evaluate modern machine learning methods for DNA sequence-to-function models with applications in research biology
Leverage sequence-to-function models (and related foundation models) to improve downstream tasks including variant effect prediction
Build rigorous benchmarking and analysis pipelines (e.g., stratified evaluation, robustness checks, and model comparison studies) to drive clear, defensible conclusions
Predict and contextualize the impact of variants using sequence models at a high level
Present research findings to mentors and stakeholders across AIBT, CBM, and Research Biology, and contribute to internal reports and (where appropriate) publications or conference presentations
Train or fine-tune sequence models using genomics data such as ATAC-seq, RNA-seq, ChIP-seq, and/or MPRAs (depending on project needs and data availability)
Collaborate closely with machine learning scientists and biologists Ownership of challenging and impactful projects with real scientific relevance
Work with scientists and engineers across ML and biology in the biotechnology industry
Deliver a final presentation of project work to a broader internal audience
Professional & personal development opportunities
Qualification
Required
Masters, or pursuing a PhD, or recent PhD graduate in one of the following fields: Computational Biology, Computer Science, Biology, or related computational fields
Familiarity with sequence modeling concepts and workflows; experience with sequence foundation models (e.g., Decima, Borzoi, Enformer, DNA/RNA language models, or related)
Comfort reading and implementing ideas from recent ML literature including model calibration and applying them pragmatically to improve model performance and evaluation
Strong research interest in genomics, regulatory genomics, variant interpretation, or the non-coding genome
Fluent in Python and experience with modern deep learning frameworks (e.g., PyTorch or TensorFlow)
Prior educational exposure to basic concepts in cell biology and gene expression
Preferred
Demonstrated ability to communicate complex computational biology concepts to both technical and non-technical audiences (e.g., publications, talks, or public code repositories)
Familiarity with regression-based statistical finemapping models (e.g., SuSiE, FINEMAP, PAINTOR) to identify causal variants within genomic loci
Experience applying sequence models to biological interpretation, in silico experimentation, or design-oriented workflows
Familiarity with common genomics data types and tooling (genome annotations, intervals, and large-scale datasets)
Curiosity about cis-regulatory elements (enhancers, promoters) and how they influence gene expression across cell states and disease contexts
A collaborative mindset and enthusiasm for bridging ML and biology across teams
Benefits
Professional & personal development opportunities.
Company
BayOne Solutions
BayOne Solutions provides computer programming services.
H1B Sponsorship
BayOne Solutions has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (23)
2024 (25)
2023 (20)
2022 (30)
2021 (20)
2020 (37)
Funding
Current Stage
Late StageRecent News
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