Research Scientist, Machine Learning, Multi Modality

What You Will Contribute To Altos

As a Scientist, Machine Learning, you will help to accelerate and optimize our progress in developing foundation models for multiscale biology, in multi-modal biological generative modeling. We are looking for a creative and collaborative individual to join our multidisciplinary team of scientists and engineers building the computational platforms that will enable Altos to achieve its mission. The successful candidate will thrive in a fast-paced environment that stresses teamwork, transparency, scientific excellence, originality, and integrity.

Responsibilities

• Work on a team of engineers and scientists to train and optimize large-scale machine learning systems using multimodal biological data and natural language data.
• Help discover and develop new exciting applications of the models that we develop to health and biology.
• Collaborate closely with experimentalists to design and execute on data collection efforts to be used in computational modeling efforts.
• Continuously learn and stay up-to-date on the latest developments in deep learning for biological discovery.
• Partner with other machine learning scientists and engineers to establish automated, robust, and efficient analytical pipelines for reproducible research.
• Contributes to seminars and other scientific initiatives within Altos and the broader scientific community.

Who You Are

Minimum Qualifications

• PhD 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, explainability, diffusion models.
• Proven track record in training and developing deep learning models.
• Someone with a highly collaborative mindset, who is self-motivated
• Ability to communicate and explain the design, results, conclusions and the impact of findings to both scientific and nonscientific staff.
• Deep analytical thinker and problem solver.

Preferred Qualifications

• Track record of ML applied to NGS data, including RNA-seq, ATAC-seq, ChIP-seq, DNA methylation, and others.
• Track record of ML applied to biological imaging modalities, including microscopy, H&E, IF.
• Experience in analysis and processing of spatial transcriptomics data, including integration with imaging-based data modalities (H&E and/or IF).
• Familiarity with multimodal data integration, including early and/or late fusion strategies.