Staff Machine Learning Scientist, Translational AI
New
United StatesFull-TimeStaff
SalaryCompetitive compensation aligned with experience and expertise
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Job Details
- Experience
- 5+ years
- Required Skills
- Machine LearningPyTorchDeep Learning
Requirements
- PhD in Computer Science, Computational Biology, Bioinformatics, Biomedical Engineering, or a closely related quantitative field.
- 5+ years of experience applying deep learning to genomic, clinical, or multi-omic datasets, ideally in oncology or immunology contexts.
- Strong expertise in transformer architectures, representation learning, self-supervised learning, and sequence modeling.
- Proven ability to translate ML model outputs into clinically or biologically meaningful insights, not just offline metrics.
- Advanced proficiency in PyTorch and modern ML ecosystems such as Hugging Face, PEFT frameworks, and distributed training systems.
- Experience leading end-to-end model development, including architecture design, experimentation, and deployment readiness.
- Strong understanding of experimental design, statistical validation, and model evaluation in high-stakes environments.
- Excellent communication skills with the ability to bridge technical, biological, and clinical stakeholders.
Responsibilities
- Lead the design, training, and evaluation of foundation models for genomic, transcriptomic, and multimodal biological data in oncology and translational medicine.
- Develop post-training, fine-tuning, and parameter-efficient adaptation workflows to align large models with real-world clinical and molecular datasets.
- Design rigorous validation frameworks that connect model outputs to biological signals, clinical outcomes, and real-world evidence.
- Build and optimize deep learning systems for tasks such as biomarker discovery, recurrence monitoring, and treatment response prediction.
- Identify and mitigate dataset bias, covariate shift, and model failure modes in clinically sensitive AI pipelines.
- Translate complex biological and clinical requirements into structured machine learning problems and scalable model architectures.
- Collaborate across AI Research, Computational Biology, and Clinical Science teams to ensure reproducibility and translational validity of models.
- Contribute to scientific communication through technical documentation, publications, and presentations in academic and industry forums.
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