Scientist /Senior Scientist, Multimodal & Relational Machine Learning Foundation Models

- 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 across research and engineering boundaries.

- Self-motivated to drive and deliver on long-term technical projects and scientific goals.

- Demonstrates the desire to grow professionally and expand their skillset in biology, machine learning, and/or drug development.

- Able to communicate and explain the design, results, and impact of complex AI architectures to both scientific and non-scientific staff.

- Keen to contribute to seminars and scientific initiatives within Altos and the broader AI research community.

- PhD in Computer Science, Machine Learning, or a similar quantitative field with 5+ years of relevant work experience in academic or industry settings.

- Prior experience in developing and implementing novel generative AI models, specifically in multimodal integration, GraphRAG, or relational deep learning .

- Deep understanding of Machine Learning principles and how they apply to diverse architectures like Transformers, GNNs, and diffusion models .

- Very strong programming skills in Python and deep learning libraries (e.g., PyTorch, JAX, Hugging Face Transformers/Accelerate).

- Proven experience with multi-GPU and distributed training at scale (e.g., DDP, FSDP, DeepSpeed, Megatron, or Ray).

- Strong track record of published, peer-reviewed innovative AI/ML research at top-tier conferences (NeurIPS, ICML, ICLR, CVPR).

- Familiarity with tabular foundation models (e.g., TabPFN) and in-context learning strategies for structured data .

- Specific experience in native multimodal modeling (early-fusion) or the synthesis of LLMs and Knowledge Graphs .

- Track record of ML applied to biological data, such as NGS data (RNA-seq, ATAC-seq), biological imaging (microscopy, IF), or spatial transcriptomics.

- Experience in optimizing large-scale inference via quantization, distillation, or memory-efficient attention mechanisms.