Job Title: Senior Researcher Location: Bay Area or NYC · Onsite Employment: Full-time Compensation: $200k – $300k About the Organization An innovative AI-driven biotech startup specializing in integrating high-throughput biology with generative AI technology to accelerate the discovery and development of therapeutic antibodies. The company’s platform has been validated through multiple partnerships with pharmaceutical companies, providing access to differentiated antibodies that are challenging to identify with traditional methods. Their digital biology and AI platform enables rapid Generate-Test-Learn-Optimize cycles for high-throughput, function-focused antibody design, targeting areas such as immunology, inflammation, and metabolic diseases, with a mission to improve human health through digital therapeutic discovery. The Role The organization is seeking a Senior Researcher to develop and train generative models central to their antibody discovery platform. The focus will be on antibody design, protein structure prediction, and broader biomolecular modeling. The candidate will work with large-scale deep learning models trained on proprietary high-throughput data, directly contributing to iterative design and validation cycles. This position offers an opportunity to design antibodies computationally, observe high-throughput testing results, and refine models based on experimental feedback. Responsibilities Model Development & Research • Design, train, and assess deep learning models for antibody and protein engineering, including structure prediction and generative architectures. • Create models that predict binding affinity, maturation potential, developability, and facilitate de novo antibody design. • Investigate novel architectures, contribute original research, and publish findings as appropriate. Data & Platform • Utilize large proprietary antibody datasets generated via high-throughput biological assays to train and improve models. • Integrate computational models with experimental data, closing the loop between in silico predictions and wet-lab validation. • Scale models across extensive GPU clusters, employing hundreds or thousands of GPUs in parallel computing environments. Collaboration & Impact • Collaborate with protein engineers, biologists, and platform engineers to translate models into therapeutic candidates. • Engage with the broader research community through open-source contributions and academic dissemination. Qualifications Essential • Deep expertise in antibody design, protein modeling, or structural biology, including hands-on model training experience in these areas. • Practical experience with deep learning frameworks (e.g., PyTorch or JAX), especially in training large models from scratch. • Experience with distributed training on multiple GPUs. • Possession of a PhD or Master’s degree in machine learning, computational biology, computer science, or related fields, or an equivalent research record. • Demonstrated research through publications at top-tier conferences such as NeurIPS, ICML, or ICLR. • A strong drive for innovation and a passion for foundational progress in the space. Preferred • Experience working with generative models for protein or antibody sequence and structure design. • Knowledge of experimental antibody discovery workflows such as display technologies or high-throughput screening. • Contributions to open-source modeling tools or frameworks used by the research community.