Machine Learning Scientist / Senior Machine Learning Scientist, Virtual Cell in Cambridge

Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life.

Diversity at Altos: We believe that diverse perspectives are foundational to scientific innovation and inquiry. At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment.

What You Will Contribute to Altos: This is an opportunity to join the state-of-the-art Virtual Cell team that recently won the Generalist prize in the ARC Virtual Cell Challenge. Here you will help to accelerate and optimize our progress in developing multi-modal generative foundation models for multiscale biology. In this role, you will be an integral part of our multidisciplinary teams enabling Altos to achieve its mission. You will partner and collaborate with other Machine Learning Scientists and Engineers, as well as other computational scientists and biologists, across the Institute of Computation to contribute to the Altos research and translation ecosystem. This role is focused on improving our state-of-the-art “virtual cell” models, encompassing gene and protein modeling, imaging, and other modalities to aid in the discovery of novel interventions for aging and disease. The successful candidate will thrive in a fast-paced environment that emphasises teamwork, transparency, scientific excellence, originality, and integrity.

Responsibilities:

• Use your experience to focus on designing, developing and evaluating state of the art foundation and focused models, at scale, to advance the Altos mission.
• Pre-train and fine-tune large-scale machine learning systems using multimodal biological data and prior knowledge inputs.
• Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models, diffusion/flow matching models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation.
• Design, implement, and optimize large-scale machine learning systems using modern frameworks (e.g., PyTorch, JAX), AI-assisted coding, and agile practices.
• Develop and manage efficient distributed training strategies across multiple GPUs and compute clusters to handle terabytes of multi-modal biological data.
• Develop robust approaches for multi-modal data integration and cross-domain mapping to extract actionable biological insights.
• Participate in the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation.

Who You Are:

• Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities.
• Highly collaborative in mindset and ways of working.
• Self-motivated to drive and deliver on projects and goals.
• Focused on professional growth and expanding your skillset and knowledge.
• Able to communicate and explain the design, results, conclusions and the impact of their work to both scientific and nonscientific staff.
• Able to stay up-to-date on the latest developments in deep learning and apply knowledge to their work.
• Keen to take the opportunity to contribute to seminars and other scientific initiatives within Altos and the broader scientific community.

Minimum Qualifications:

• Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Statistics, or a related quantitative field, demonstrating a deep theoretical foundation in ML/AI.
• Relevant work experience in either an academic or industry setting.
• Prior experience in developing and implementing novel generative AI models in a subset of the following: transformers, multi-modality, diffusion/flow matching models.
• Can demonstrate a deep understanding and expertise of Machine Learning Principles and how they apply to different models.
• Proven experience developing and applying complex machine learning models, preferably with a significant portion of that time spent in a fast-paced industry or translational research environment.
• Very strong programming skills, including experience with Python and deep learning libraries (PyTorch, Hugging Face Transformers, H-F Datasets, H-F Accelerate).
• Experience writing production-quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar; Experience with multi-GPU and distributed training at scale.

Preferred Qualifications:

• Strong track record of published peer-reviewed innovative AI/ML research.
• Experience in cell health and rejuvenation related research area.
• Experience in the application of machine learning methods to biological data.
• Experience in computational approaches to drug discovery.
• Experience with software development methodologies and open-source software.

The salary range for Cambridge, UK: Exact compensation may vary based on skills, experience, and location.

Equal Opportunity Employment: We value collaboration and scientific excellence. We believe that diverse perspectives and a culture of belonging are foundational to scientific innovation and inquiry. At Altos Labs, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining an inclusive environment. Altos Labs provides equal employment opportunities to all employees and applicants for employment, without regard to race, colour, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Altos prohibits unlawful discrimination and harassment. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

Thank you for your interest in Altos Labs where we strive for a culture of scientific excellence, learning, and belonging.