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Machine Learning Scientist / Senior Machine Learning Scientist, Virtual Cell
Исключительная вакансия в одной из самых амбициозных биотехнологических компаний мира с отличным финансированием. Предлагает работу над передовыми научными задачами, конкурентную заработную плату и возможность внести вклад в борьбу со старением.
Сложность вакансии
Высокая сложность обусловлена требованием степени Ph.D., глубоких знаний в области генеративного ИИ (диффузионные модели, трансформеры) и опыта работы с крупномасштабными биологическими данными. Роль требует сочетания продвинутых навыков программирования и понимания фундаментальной биологии.
Анализ зарплаты
Предлагаемая зарплата (от £74,900 до £152,100 в зависимости от грейда) полностью соответствует или даже несколько превышает рыночные стандарты для ML-ученых в Кембридже, учитывая престижность компании и сложность задач. Кембридж является дорогим регионом, но данные уровни компенсации обеспечивают высокий уровень жизни.
Сопроводительное письмо
I am writing to express my strong interest in the Machine Learning Scientist position within the Virtual Cell team at Altos Labs. With a deep background in developing generative foundation models and a passion for applying AI to complex biological systems, I am inspired by your mission to restore cell health and reverse age-related diseases. My experience in pre-training large-scale multimodal models and implementing distributed training strategies aligns perfectly with your current initiatives in multiscale biology.
In my previous work, I have successfully designed and optimized transformer-based architectures and diffusion models, often handling terabytes of data across multi-GPU clusters. I am particularly drawn to Altos Labs' collaborative environment and the opportunity to work at the intersection of machine learning and life sciences. I am confident that my technical expertise in PyTorch and JAX, combined with my commitment to scientific excellence, will allow me to make significant contributions to the Virtual Cell project and the broader Altos research ecosystem.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в altoslabs уже сейчас
Присоединяйтесь к команде мирового уровня в Altos Labs и создавайте будущее биологии с помощью генеративного ИИ!
Описание вакансии
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.
For more information, see our website at altoslabs.com.
Our Value
Our Single Altos Value: Everyone Owns Achieving Our Inspiring Mission.
Diversity at Altos
Altos Labs has been named one of the Top 3 Biotech Companies and ranked for the second year on the Forbes 2026 Best Startups in America list. 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/emphasizes \*please spell differently for UK/US 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 you 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:
- Scientist I, Machine Learning: £74,900 - £98,500
- Scientist II, Machine Learning: £107,900 - £142,000
- Senior Scientist I, Machine Learning: £115,600 - £152,100
Exact compensation may vary based on skills, experience, and location.
Before submitting your application:
- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.
#LI-NN1
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, 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.
Note: Altos Labs will not ask you to download a messaging app for an interview or outlay your own money to get started as an employee. If this sounds like your interaction with people claiming to be with Altos, it is not legitimate and has nothing to do with Altos. Learn more about a common job scam at https://www.linkedin.com/pulse/how-spot-avoid-online-job-scams-biron-clark/
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Machine Learning
- Deep Learning
- Python
- PyTorch
- JAX
- Generative Models
- Transformers
- Diffusion Models
- Distributed Training
- Hugging Face
- Statistics
- Bioinformatics
Возможные вопросы на собеседовании
Проверка опыта работы с архитектурами, упомянутыми в вакансии, применительно к биологическим данным.
Как бы вы адаптировали архитектуру трансформера для обработки мультимодальных биологических данных, таких как последовательности генов и изображения клеток?
Вакансия требует опыта работы с распределенным обучением на больших кластерах.
Опишите ваш опыт оптимизации распределенного обучения (DDP, DeepSpeed или FSDP) для моделей с миллиардами параметров. С какими узкими местами вы сталкивались?
Важно понять, как кандидат оценивает качество генерации в специфической научной области.
Какие метрики вы считаете наиболее релевантными для оценки точности генеративной модели «виртуальной клетки»? Как убедиться в биологической достоверности результатов?
Роль предполагает работу на стыке дисциплин.
Расскажите о случае, когда вам приходилось объяснять сложные концепции машинного обучения коллегам-биологам. Как вы обеспечили эффективное взаимодействие?
Проверка навыков работы с современными фреймворками.
В каких сценариях вы бы предпочли использовать JAX вместо PyTorch для разработки моделей клеточного омоложения и почему?
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- Страна
- Великобритания
- Зарплата
- 74 900 ₽ – 152 100 ₽