Machine Learning Engineer/Machine Learning Scientist , Multi Modality

Machine Learning Engineer / Machine Learning Scientist, Multi Modality 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.

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

As part of our team, you will help accelerate and optimize progress in developing multi-modal generative foundation models for multiscale biology. We are open to hiring either a Machine Learning Scientist or Machine Learning Engineer. In either role, you will be an integral part of multidisciplinary teams building the computational platforms that enable Altos to achieve its mission. You will partner and collaborate with other Machine Learning Scientists and Engineers across the Institute of Computation to contribute to the Altos research and translation ecosystem, focusing on designing and building state‑of‑the‑art multimodal foundation models that tackle biological questions and aid in the discovery of novel interventions for aging and disease.

Responsibilities Machine Learning Engineer Build, deploy, and optimize 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 strategies and performance tracking to train large models with distributed training across multiple nodes. Apply software engineering skills to develop reliable, scalable, and performant distributed systems in a cloud environment. Machine Learning Scientist Design, develop, and evaluate state‑of‑the‑art foundation models at scale to benefit research. Pre‑train and fine‑tune large‑scale machine learning systems using multimodal biological data and natural language inputs. Gain insights based on theory, deep research, and the mathematical underpinnings of your work. Use AI as a tool to accelerate research. Apply strong coding experience to model development using existing languages and frameworks. Who You Are Excited about 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. Demonstrates a desire to grow professionally and expand skillset and knowledge; keen to learn more about biology, computational science, and drug development. Can communicate and explain the design, results, conclusions, and impact of work to both scientific and non‑scientific staff. Stays up‑to‑date on the latest developments in deep learning and applies that knowledge to work. Keen to contribute to seminars and other scientific initiatives within Altos and the broader scientific community. Minimum Qualifications Machine Learning Engineer MS in Computer Science, Statistics, Machine Learning, Artificial Intelligence, or related discipline. 0–5 years of relevant work experience in academic or industry settings. Very strong programming skills, including Python and deep learning libraries (PyTorch, Hugging Face Transformers, H‑F Datasets, H‑F Accelerate). Ideally experience in a distributed training framework such as DDP, FSDP, Deepspeed, Megatron, or Hugging Face Accelerate, Ray. Expertise in transformers, natural language processing, multimodality in language or biology, diffusion models, or a subset thereof. Machine Learning Scientist PhD in Computer Science, Machine Learning, or similar fields. 0–5 years of relevant work experience in academic or industry settings. Prior experience developing and implementing novel generative AI models in a subset of transformers, multimodality, diffusion models. Demonstrated deep understanding and expertise of machine learning principles and their application to different models. Very strong programming skills, including Python and deep learning libraries (PyTorch, Hugging Face Transformers, H‑F Datasets, H‑F Accelerate). Strong track record of peer‑reviewed AI/ML research publications. Experience writing production‑quality code with modern machine learning frameworks (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. Salary Range

Machine Learning Scie