Machine Learning Engineer, Computer Vision

What You Will Contribute To Altos

At Altos Labs, our mission to unravel the mysteries of cell rejuvenation and human health hinges on groundbreaking quantitative solutions. The role of a Machine Learning Engineer is a pivotal architect in building the high-performance, scalable systems that translate complex biomedical imagery and multi-omics data into actionable insights. Machine Learning engineers at Altos directly enable and accelerate the mission by pioneering state-of-the-art computer vision and machine learning applications. The best candidate has expertise that bridges the gap between cutting-edge scientific discovery and robust, accessible computational tools. The contributions will be profoundly impactful, driving innovation across multiple scales of biomedical data – from Electron/Light Microscopy and Digital Histology/Pathology to sophisticated In Vivo functional analysis. Collaborating seamlessly with our ML Ops team, we aim to ensure our models are not just powerful, but also easily trainable, discoverable, interpretable, and universally accessible across diverse research groups.

Responsibilities:

• Pioneer Model Development & Optimization: The Machine Learning Engineer will be at the forefront, meticulously evaluating and re-engineering state-of-the-art AI models across the entire spectrum of imaging. This includes developing solutions for de novo protein design, structure identification, and dynamics in single-particle CryoEM, as well as integrating light microscopy and multi-omics data for cross-domain mapping of in situ and in vivo collected data. Your deep technical acumen will transform complex algorithms into practical, high-impact tools.
• Architect Scalable Distributed Systems: Leverage deep software engineering skills to design, develop, and implement reliable, performant, and inherently scalable distributed systems within a dynamic cloud environment. The solutions proposed will form the backbone of our computational infrastructure, handling massive, intricate datasets with precision and efficiency.
• Optimize Data Pipelining for Exascale Training: Take ownership of developing highly efficient data loading strategies and robust performance tracking mechanisms essential for training colossal models. This involves expertly orchestrating distributed training across multiple compute nodes, pushing the boundaries of what’s possible in large-scale machine learning.
• Forge Integrated Analysis Pipelines: Engineer, deploy, and meticulously manage complex multi-modal analysis pipelines that serve as the bedrock for scientific analysis and sophisticated machine learning workflows. The vision is to culminate in a unified, intuitively usable framework that empowers our scientists.
• Bridge the Technical and Scientific Divide: Serve as the essential communication conduit, adeptly translating complex technical concepts between experimental scientists, advanced algorithm developers, and deployment engineers. Your ability to foster clear, effective communication will ensure seamless integration and successful project execution.
• Drive Technical & Cultural Excellence: Proactive force in designing and championing technical and cultural standards across both scientific and engineering functions. Your leadership will ensure best practices in code quality, collaboration, and continuous innovation.

Who You Are

Minimum Qualifications

• BS/MS in Computer Science, Biomedical Engineering, or a closely related quantitative field.
• 2-5 years of direct, hands-on experience in relevant industry and/or academic settings, showcasing your ability to deliver tangible results.
• Mastery of core programming languages critical for large-scale data management and machine learning, including Python, C++, and deep proficiency with frameworks like PyTorch/TensorFlow, and PyTorch Lightning.
• Demonstrable expertise in Machine Learning at scale, with practical experience in Large Language Models, Self-Supervised/Contrastive/Representation Learning for Computer Vision applications, and multi-modal data integration.
• Proven capability in applying rigorous software engineering practices within a scientific or similarly demanding, high-stakes environment.
• A strong, demonstrable track record of hands-on technical leadership and significant scientific contributions, as evidenced by publications or conference presentations.
• An innate enthusiasm to design, implement, and champion technical and cultural standards that elevate our entire scientific and technical ecosystem.

Preferred Qualifications

• Prior experience with bioinformatics data processing and analysis, showcasing a relevant domain understanding.
• Expertise in multi-source data integration, solving complex challenges in disparate datasets.
• Practical experience with cloud computing platforms and containerization technologies, enabling scalable deployments.
• Knowledge of genetics and/or human genetics, further enhancing your ability to contribute to our core mission.

The salary range for San Francisco Bay Area, CA:

• Machine Learning Engineer I: $153,000 – $207,000
• Machine Learning Engineer II: $178,500 – $241,500

The salary range for San Diego, CA:

• Machine Learning Engineer I: $150,450 – $203,550
• Machine Learning Engineer II: $170,000 – $230,000

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