Director of Machine Learning

• In partnership with the Executive Director and in collaboration with the Allen Technology Office, define and own the ML strategy for the enhancer flywheel and additional synthetic biology flywheels, including success metrics and roadmaps
• Build and manage a central ML team, plus ML/data scientists embedded in project teams
• Architect and implement sequence-to-function and generative models for regulatory element and other DNA, RNA, and protein design, leveraging state-of-the-art architectures (CNNs, transformers, diffusion, etc.)
• Design and optimize DBTL loops via collaboration with project teams, e.g., supporting assay design, active learning tactics, assay configuration, and benchmarking
• Supervise quantitative analysis and QC of high-throughput assays (e.g., MPRA, single-cell data), integrating external datasets such as scATAC-seq and RNA-seq for transfer learning
• Prioritize projects based on organizational goals, collaborating cross-functionally to ensure timely, high-quality delivery
• Establish ML best practices across projects (code quality, experiment tracking, model and data versioning, documentation, reproducibility)
• Partner with data/engineering teams in the Office of the CTO to define and maintain the computational infrastructure required for large-scale sequence modeling and genomics data integration
• Serve as the primary program ML representative, clearly communicating strategy, trade-offs, and results to project leads, leadership, and external collaborators, and contributing to publications and presentations
• Propose and develop ML partnerships across academia, biotech, non-profits, and industry in support of our mission

Note: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

• Ph.D. in Computer Science, Computational Biology, Statistics, Physics, or related field; or equivalent combination of degree and experience
• 5+ years of post-Ph.D. (or equivalent) experience building, training, and deploying ML models in a research or product environment
• Deep expertise in ML applied to biological sequences or structured biological data (e.g., regulatory genomics, transcriptional modeling, protein/DNA design)
• Strong proficiency in Python and at least one modern ML framework (e.g., PyTorch, JAX, or TensorFlow)
• Proven track record of technical leadership: mentoring scientists/engineers, setting standards, and delivering complex ML systems
• Excellent communication skills and ability to collaborate effectively with both computational and experimental scientists

• Demonstrated experience integrating diverse datasets (e.g., ATAC-seq, RNA-seq, single-cell data) into predictive or generative models
• Research experience in regulatory genomics, enhancers/promoters, transcription factor binding, or MPRA-based model training
• Experience with AI-driven protein design tools such as RFdiffusion, ProteinMPNN, or comparable workflows
• Hands-on work with DBTL loops in synthetic biology, including active learning, experiment selection, or closed-loop optimization
• Experience with generative models for biological sequences (e.g., autoregressive, VAE, diffusion, RL-based sequence design)
• Prior experience leading ML efforts in small, fast-moving, or start-up-style research environments
• Strong publication or open-source record in ML for biology, sequence modeling, or synthetic biology

• Fine motor movements in fingers/hands to operate computers and other office equipment

• This role is currently working onsite and is expected to work onsite four days/week. The primary work location for this role is 700 Dexter Ave N., with the flexibility to work remotely on a limited basis. We are a Washington State employer, and any remote work must be performed in Washington State.

• Attendance and participation in national and international conferences as appropriate

• **Please note, this opportunity offers relocation assistance**
• **Please note, this opportunity may offer work visa sponsorship**

• $224,200 - $294,250 *

* Final salary depends on the required education for the role, experience, level of skills relevant to the role, and work location, where applicable.