Staff Machine Learning Scientist, Translational AI

POSITION SUMMARY:

We are seeking a Staff Machine Learning Scientist Translational AI to provide technical leadership at the intersection of deep learning foundation models, computational biology, and molecular diagnostics. This ownership role drives the architecture and validation of genomic, transcriptomic, and multimodal sequence models to accelerate patient stratification, target identification, and therapeutic monitoring across our cell-free DNA (cfDNA) and multi-omic platforms. This Staff-level position operates with broad technical autonomy, driving modeling strategy across multiple concurrent portfolios while maintaining direct execution responsibilities in model compilation, scaling, and testing. Working within a builder framework, you will align across AI Research, Bioinformatics, and Clinical Science divisions to transition advanced representation learning models into reproducible, clinically valid diagnostic assets.

PRIMARY RESPONSIBILITIES:

Scientific Leadership in Translational AI

• Serve as the principal technical authority on the deployment of molecular, genomic, and pathology foundation models applied to oncology and translational medicine questions
• Engineer rigorous alignment and post-training workflows that ground pre-trained foundation models in empirical clinical trial and molecular diagnostic data, eliminating speculative modeling assumptions
• Formulate objective peer-review frameworks and deliver technical feedback to elevate the modeling code, experimental standards, and scientific designs of the broader AI research group

Foundation Models to Biological and Clinical Translation

• Lead the post-training, parameter-efficient fine-tuning (PEFT), and evaluation of deep sequence, multimodal, and representation learning models for biomarker discovery, molecular recurrence monitoring, and therapeutic response forecasting
• Design robust fine-tuning, probing, and latent space representation analysis workflows that extract interpretable, biologically grounded patterns from high-dimensional transformer architectures
• Validate model outputs against multi-omic benchmarks and real-world outcomes, ensuring model predictions deliver the exact deterministic accuracy required for patient tracking and clinical interventions

Modeling, Experimentation, and Evaluation

• Build, train, and optimize advanced machine learning models utilizing next-generation sequencing (NGS), ctDNA assays, digital pathology imaging, and longitudinal clinical metadata
• Design rigorous clinical investigation and evaluation frameworks that connect model performance metrics (e.g., loss curves, precision-recall) directly to translational utility and real-world distribution shifts
• Systematically identify algorithmic failure modes, sources of dataset bias, and covariate shift, implementing robust mitigation strategies suitable for regulated, clinical-facing pipelines

Cross-Functional Collaboration and Influence

• Partner with Computational Biology, Translational Science, and Medical Affairs teams to translate complex clinical requirements into clear, quantitative machine learning problem statements
• Act as a systems-level technical bridge between AI Research and ML Engineering teams to ensure that validation models convert seamlessly into scalable, reproducible production workflows
• Provide technical leadership and data execution support for strategic external collaborations, pharmaceutical partnerships, and foundation model research consortiums

Scientific Communication and External Presence

• Translate complex multimodal model architectures and performance metrics into transparent, high-integrity data packages for clinical governance, leadership updates, and external collaborators
• Lead the authoring of technical manuscripts for peer-reviewed machine learning venues (e.g., NeurIPS, ICML, ICLR) and major computational biology journals
• Act as a technical representative for the company's translational AI capabilities at international medical, oncology, and machine learning conferences

QUALIFICATIONS:

• PhD in Computer Science, Computational Biology, Bioinformatics, Biomedical Engineering, or a highly quantitative structural field
• 5+ years of industry or post-doctoral experience applying deep learning frameworks to complex biological, genomic, or clinical datasets, with a documented focus on oncology or immunology portfolios
• Deep technical competency with transformer architectures, representation learning, self-supervised learning (SSL), or deep sequence modeling
• Proven track record of translating machine learning outputs into verifiable biological variables or clinical performance indicators, rather than optimizing solely for isolated cross-validation metrics
• Expert proficiency in PyTorch and modern machine learning infrastructure (e.g., HuggingFace ecosystem, PEFT, Captum, MLflow, and distributed GPU computing setups)
• Documented technical leadership through end-to-end project ownership, architectural design authority, or cross-functional team direction

Preferred Qualifications:

• Experience constructing or fine-tuning multimodal foundation models that combine high-depth genomic sequencing data with digital pathology images or longitudinal electronic health records (EHR)
• Direct experience handling clinical trial datasets, real-world data (RWD/RWE), or developing models within health-authority/regulatory-facing frameworks
• Strong record of publications as primary author in high-impact machine learning venues

KNOWLEDGE, SKILLS, AND ABILITIES:

• Advanced mathematical and algorithmic fluency across deep learning methodologies, optimization strategies, and probabilistic modeling
• Fast learner with the capability to master complex cfDNA platforms, biochemistry workflows, and multi-omic data generation pipelines rapidly
• Precise written and verbal communication styles with strict attention to algorithmic detail and statistical validation boundaries
• Proven capability to drive independent portfolios while executing cross-functional objectives within matrixed technology and scientific teams
• High-growth builder mindset with the capability to balance scientific rigor, operational execution speed, and computational resource constraints under tight timelines
• Utilize cloud-based productivity and high-performance computing infrastructure to maintain high operational momentum in a fast-evolving artificial intelligence environment