Job Description
As an Artificial Intelligence Researcher, you will build production-grade machine learning systems for bioinformatics applications. You will partner closely with genomic analysts, diagnosticians, data engineers, and bioinformaticians to develop models and AI-enabled services that operate at scale across omics, clinical, and operational datasets, turning raw, messy data into reliable signals, predictions, and decision-support outputs.
This is a hands-on role spanning ML research and applied engineering, with strong emphasis on data quality, reproducibility, MLOps, and integration into bioinformatics pipelines.
Role and Responsibilities
- Design and develop AI/ML solutions leveraging genomics/NGS, proteomics, methylation, transcriptomics, histopathology, and other clinical datasets.
- Build end-to-end pipelines across the model lifecycle.
- Apply statistical learning and ML to problems such as variant prioritization/ranking, cancer detection, rare disease classification etc.
- Develop NLP solutions to analyze unstructured clinical notes to support clinical and diagnostic decision-making.
- Implement scalable training and inference workflows using distributed compute and batch/stream processing where needed.
- Build and maintain model monitoring: drift detection, performance tracking, data quality alerts, and post-deployment audits.
- Document assumptions, model limitations, and validation results for internal stakeholders (bioinformatics, clinical, product).
Must Have
- 3+ years of experience (or equivalent skill level) training machine learning models, with strong understanding of ML fundamentals (optimization, generalization, evaluation, and error analysis). (Must know what’s going on under the hood!)
- 3+ years of hands-on experience with ML libraries such as scikit-learn, Py Torch, Tensor Flow, Hugging Face, and LLM application tooling (e.g., Lang Chain/Llama Index).
- 1+ years of experience with fine-tuning and prompting for language models and vision-language models
Good to Have
- Background in Computational Biology or Bioinformatics. Familiarity with common bioinformatics data types (e.g., FASTQ/BAM/VCF).
- Experience with at least one cloud platform: AWS, GCP, or Azure.
This is a hands-on role spanning ML research and applied engineering, with strong emphasis on data quality, reproducibility, MLOps, and integration into bioinformatics pipelines.
Role and Responsibilities
- Design and develop AI/ML solutions leveraging genomics/NGS, proteomics, methylation, transcriptomics, histopathology, and other clinical datasets.
- Build end-to-end pipelines across the model lifecycle.
- Apply statistical learning and ML to problems such as variant prioritization/ranking, cancer detection, rare disease classification etc.
- Develop NLP solutions to analyze unstructured clinical notes to support clinical and diagnostic decision-making.
- Implement scalable training and inference workflows using distributed compute and batch/stream processing where needed.
- Build and maintain model monitoring: drift detection, performance tracking, data quality alerts, and post-deployment audits.
- Document assumptions, model limitations, and validation results for internal stakeholders (bioinformatics, clinical, product).
Must Have
- 3+ years of experience (or equivalent skill level) training machine learning models, with strong understanding of ML fundamentals (optimization, generalization, evaluation, and error analysis). (Must know what’s going on under the hood!)
- 3+ years of hands-on experience with ML libraries such as scikit-learn, Py Torch, Tensor Flow, Hugging Face, and LLM application tooling (e.g., Lang Chain/Llama Index).
- 1+ years of experience with fine-tuning and prompting for language models and vision-language models
Good to Have
- Background in Computational Biology or Bioinformatics. Familiarity with common bioinformatics data types (e.g., FASTQ/BAM/VCF).
- Experience with at least one cloud platform: AWS, GCP, or Azure.
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