Senior / Principal Machine Learning Scientist - San Diego - Altos Labs

Description

Senior / Principal Machine Learning Scientist at Altos Labs

We invite you to apply for the Senior / Principal Machine Learning Scientist role. The ideal candidate will be a hands on, creative, and collaborative scientist eager to lead transformative AI research in aging and disease.

Mission

Restore cell health and resilience through cell rejuvenation to reverse disease, injury, and age related disabilities.

Values

Everyone Owns Achieving Our Inspiring Mission. Diversity, belonging, and inclusion are foundational to our success.

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 optimize 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 organization.

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.
• Team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focused 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 to 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, bio imaging).

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 Range

Redwood City, CA:

• Senior Scientist I, Machine Learning: $280,500 - $379,500
• Senior Scientist II, Machine Learning: $317,050 - $428,950
• Principal Scientist, Machine Learning: $376,550 - $509,450

San Diego, CA:

• Senior Scientist I, Machine Learning: $260,950 - $353,050
• Senior Scientist II, Machine Learning: $285,600 - $386,400
• Principal Scientist, Machine Learning: $340,000 - $460,000

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 ).
• This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

Equal Opportunity Employment

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.

We value collaboration and scientific excellence, and we strive for a culture of belonging and inclusion.