Senior / Principal Machine Learning Scientist

Altos Labs; Cambridge; On-site; GBP 80,000 - 110,000; Full time

Job summary

A pioneering biotechnology firm is seeking a Senior / Principal Machine Learning Scientist in Cambridge, UK. This role involves developing innovative AI/ML models to advance research in cell health and rejuvenation. Candidates should have extensive experience in machine learning techniques and a strong background in leading significant research efforts. The position offers a dynamic team environment emphasizing collaboration and scientific integrity.

Qualifications

• Proven track record leveraging machine learning to solve real‑world problems.
• Experience writing production‑quality code with modern machine learning frameworks.
• Strong track record of leading peer‑reviewed research.

Responsibilities

• Pioneer novel machine learning methodologies for cell health.
• Design and optimise large‑scale machine learning systems.
• Act as a key technical mentor to team members.

Skills

Machine learning expertise

Statistical frameworks

Generative models

Deep learning

Causal reasoning & inference

Collaboration

Education

Ph.D. in relevant field

Tools

PyTorch

TensorFlow

JAX

Job description

Senior / Principal Machine Learning Scientist

Cambridge, UK

Our Mission

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

As a Senior or Principal Machine Learning Scientist, you will play a prominent role in developing generative AI/ML models for multi‑modal, multiscale biology from virtual cells to agentic target assessment. We are looking for a hands‑on, creative, and collaborative individual to join our multidisciplinary team of scientists and engineers focused on transforming how we treat aging and disease. The successful candidate will thrive in a fast‑paced environment that emphasises teamwork, transparency, scientific excellence, originality, rigor, and integrity.

Responsibilities

• Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models, causal inference, diffusion models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation
• Contribute to setting the long‑term technical vision and research strategy for a core domain (e.g., multi‑modal data fusion, perturbation modeling) within the Institute of Computation
• Translate your deep understanding of the mathematical and theoretical underpinnings of cutting‑edge AI research into high‑impact applications
• Design, implement, and optimise large‑scale machine learning systems using modern frameworks (e.g., PyTorch, JAX) 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
• Apply computational thinking to solve problems in drug target identification, compound assessment, and prediction of cellular perturbation responses
• Lead the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation
• Act as a key technical mentor to Machine Learning Scientists and Engineers, raising the bar for scientific rigor and model robustness across the organisation.

Who You Are

• Proven track record leveraging machine learning to solve real‑world problems
• Expertise in one or more of the following: generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi‑task learning, diffusion models, graph neural networks, active learning, cooperative agents
• 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
• A team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focussed on professional growth
• Able to advise others across the wider function / company on cutting‑edge practices and approaches to enable the science / research. Desire to constantly expand your skillset and knowledge. Keen to learn more about biology, computational science, and medicine
• Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age‑related disabilities

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
• 6+ years of relevant post‑Ph.D. work experience in either an academic or industry setting
• 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
• A strong track record of leading and publishing innovative, peer‑reviewed research in top‑tier ML conferences (e.g., NeurIPS, ICML, ICLR) or high‑impact scientific journals
• Excellent scientific communication skills: verbally and in writing; with computational and non‑computational audiences, in informal 1‑1 settings, team meetings, and formal seminars
• Expertise in several of the following: deep learning, reinforcement learning, generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi‑task learning, graph neural networks, active learning, hybrid mechanistic/ML models
• Proven experience applying sophisticated ML techniques to molecular and cell biological data sets (e.g., NGS, spatial omics, bioimaging)

Preferred Qualifications

• 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

Salary

The salary range for Cambridge, UK:

Exact compensation may vary based on skills, experience, and location.

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Equal Opportunity Employment

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, color, 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.

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