Machine Learning Scientist

Company Overview We are partnering with a AI‑native therapeutics company applying frontier machine learning to understand human biology and improve clinical outcomes in oncology. The company has built one of the largest proprietary multimodal biological datasets in the industry and trains large-scale foundation models from scratch to predict therapeutic efficacy and identify patient populations most likely to benefit from treatment. Artificial intelligence is central to the company’s strategy. The team develops proprietary datasets, conducts original machine learning research, and applies advanced models directly to drug discovery. Position Summary We are seeking a Machine Learning Scientist to conduct original, high‑impact machine learning research and help develop the next generation of biological foundation models. This is a hands‑on individual contributor role. The ideal candidate is a highly capable researcher who can independently formulate ideas, rapidly implement experiments, evaluate results, and clearly communicate findings to both technical and scientific audiences. This position emphasizes research quality, scientific rigor, and intellectual contribution rather than production engineering, people management, or infrastructure ownership. Key Responsibilities Design, implement, and train foundation models on large-scale multimodal biological datasets Develop novel machine learning approaches for integrating information across biological scales and measurement modalities Define meaningful benchmark tasks and evaluation frameworks for foundation models Rapidly prototype research ideas and identify the most informative experiments to run Analyze experimental results rigorously and iterate based on findings Collaborate closely with biologists, computational scientists, and ML researchers Evaluate modern AI techniques, including large language models, for internal scientific workflows Communicate research results clearly to both technical and non-technical stakeholders Own research projects end-to-end, from initial concept through execution and conclusions Contribute to publications, conference presentations, and external scientific engagement as appropriate Required Qualifications Research Background Demonstrated excellence in original machine learning research, evidenced by: First-author publications at leading conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP), or Significant research contributions within top-tier industry research labs Strong ability to independently drive research from idea to validation Proven capability to design experiments, build models, define evaluations, and interpret results Technical Skills Deep knowledge of modern machine learning methods and architectures Strong experience implementing research models in PyTorch Familiarity with self-supervised learning, representation learning, and generative modeling approaches Education PhD strongly preferred in: Computer Science Machine Learning / Artificial Intelligence Statistics Applied Mathematics Computational Neuroscience Physics or other highly quantitative disciplines Exceptional candidates without a PhD may be considered with a compelling research track record Preferred Technical Areas Self‑Supervised Learning and Representation Learning Computer Vision Generative Models (Diffusion, Flow Matching, Autoregressive Models) Scientific Machine Learning Candidates are expected to write robust, high-quality research code, but this role is fundamentally focused on scientific insight and model development, not large-scale production systems. Why Join Work on frontier machine learning research applied to real-world biological problems Train large foundation models on proprietary, purpose-built multimodal datasets Collaborate with a highly technical, interdisciplinary team Operate in an environment where AI is core to the company’s mission Make a meaningful impact on scientific discovery and patient outcomes #J-18808-Ljbffr Harnham