Machine Learning Engineer/Machine Learning… at Altos Labs · San…

Summary: Altos Labs is focused on restoring cell health and resilience through cell rejuvenation. They are seeking a Machine Learning Engineer or Scientist to develop multi-modal generative foundation models for multiscale biology, collaborating with multidisciplinary teams to tackle biological questions and aid in the discovery of novel interventions for aging and disease. Responsibilities: - As a Machine Learning Engineer you will focus on building, deploying and optimizing machine learning models at scale - Pre-train and fine-tune large-scale machine learning systems using multimodal biological data and natural language inputs - Develop efficient data loading strategy and performance tracking to train large models with distributed training across multiple nodes - Ability to apply software engineering skills to develop reliable, scalable, performant distributed systems in a cloud environment - The ideal candidate will be able to use their experience to focus on designing, developing and evaluating state of the art foundation models, at scale, to benefit the research - Pre-train and fine-tune large-scale machine learning systems using multimodal biological data and natural language inputs - You will be able to gain insights, based on theory, deep research and the mathematical underpinnings of your work - Be experienced in using AI as a tool to accelerate your research - Can apply strong coding experience to model development using existing languages(s) and framework(s) Required Qualifications: - MS in Computer Science, Statistics, Machine Learning, Artificial Intelligence, or a related discipline - 0-5 years of relevant work experience in either an academic or industry setting - Very strong programming skills, including experience with Python and deep learning libraries (PyTorch, Hugging Face Transformers, H-F Datasets, H-F Accelerate) - Ideally, experience in a distributed training framework, like DDP, FSDP, Deepspeed, Megatron, or HuggingFace Accelerate, Ray - Expertise in a subset of the following: transformers, natural language processing, multi-modality in language and/or in biology, diffusion models - PhD in Computer Science / Machine Learning or similar fields - Prior experience in developing and implementing novel generative AI models in a subset of the following: transformers, multi-modality, diffusion models - Can demonstrate a deep understanding and expertise of Machine Learning Principles and how they apply to different models - Strong track record of published peer reviewed innovative AI/ML research - 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: - Familiarity with multimodal data integration, including early and/or late fusion strategies - Track record of ML applied to NGS data (e.g.RNA-seq, ATAC-seq, ChIP-seq, DNA methylation), biological imaging modalities (e.g. microscopy, H&E, IF), and/or spatial transcriptomics Required Skills: Python, PyTorch, Hugging Face Transformers, Hugging Face Datasets, Hugging Face Accelerate, Distributed training frameworks, Transformers, Natural language processing, Multi-modality, Diffusion models, TensorFlow, JAX, Multi-GPU training, Generative AI models, NGS data analysis, Biological imaging modalities, Spatial transcriptomics