Director, Computational & Protein Sciences

<div class="content-intro"><p><strong>About Stylus Medicine: </strong></p> <p><span class="il">Stylus</span>&nbsp;Medicine is developing transformative&nbsp;<em>in vivo</em>&nbsp;genetic medicines to unlock cures.&nbsp;<span class="il">Stylus</span>&nbsp;combines engineered recombinases with non-viral delivery to specifically encode therapeutics. The company’s approach is versatile and modular, with potential therapeutic application across oncology, autoimmune, genetic diseases, and beyond.</p></div><p><strong>About the Role:</strong></p> <p>Stylus Medicine is seeking an intellectually curious and creative Director, who will serve as a key scientific leader on our Platform team, owning the computational strategy that drives large serine recombinase (LSR) engineering and clinical translation. This role sits at the intersection of protein engineering, multi-omics analysis, and AI/ML-driven design and will lead a high-impact team spanning NGS and computational biology while driving the analytical infrastructure and predictive capabilities needed to advance our proprietary LSR platform from discovery through translation.</p> <p><strong>Key Responsibilities &amp; Accountabilities:</strong></p> <ul> <li>Lead and develop a team of NGS and computational biology scientists, setting technical direction, mentoring team members, and fostering a collaborative, high-performance culture</li> <li>Lead computational efforts to design and optimize LSRs and related genome engineering proteins for genetic medicine applications, integrating sequence-function, structure-function, and protein-DNA interaction datasets to guide engineering campaigns</li> <li>Lead analysis of NGS data from LSR experiments — including amplicon sequencing, WGS, hybrid capture, and long-read sequencing — to quantify on-target efficiency and genome-wide specificity</li> <li>Build, apply, and critically evaluate predictive and generative models for protein variant and library design — including rigorous benchmarking of AI/ML approaches such as protein language models, structure prediction tools, and generative design frameworks</li> <li>Integrate proprietary screening data with public evolutionary and structural datasets to prioritize variants and identify engineering levers for activity, specificity, and developability</li> <li>Oversee the design, implementation, and deployment of robust, scalable computational pipelines for multi-omics data analysis, ensuring accuracy, reproducibility, and maintainability</li> <li>Provide scientific input into platform strategy, translating computational insights into actionable directions for the broader team</li> <li>Partner closely with the Screening, Genome Engineering, and NGS teams to design experiments, interpret complex datasets, and refine engineering hypotheses</li> <li>Contribute to patent filings, regulatory submissions, and publications</li> </ul> <p><strong>Qualifications &amp; Skills:</strong></p> <ul> <li>PhD in Computational Biology, Bioinformatics, Protein Engineering, Machine Learning or a related quantitative discipline, with 10+ years or Master’s with 14+ years of industry experience</li> <li>Demonstrated research in protein modeling, protein design, or computational biology through publications, preprints, or equivalent industry contributions</li> <li>Hands-on experience applying AI/ML methods to protein variant and library design, including protein language models, structure prediction, and generative design approaches (e.g., AlphaFold2/3, RFdiffusion, ProteinMPNN, ESM)</li> <li>Expert-level proficiency in Python and/or R, with deep familiarity with data science libraries (e.g., Pandas, NumPy, Bioconductor, SciPy, PyTorch, or JAX)</li> <li>Experience with cloud computing platforms and workflow management systems</li> <li>Excellent communication and documentation skills, with a track record of effective collaboration in fast-paced, interdisciplinary environments</li> </ul> <p>Pay Range; $245,000 - $265,000</p>