Senior Machine Learning Engineer

We are seeking a skilled (Senior) Machine Learning Engineer to architect and implement scalable machine learning systems that integrate multi-modal omics data. This role involves developing and deploying advanced models, including graph-based, transformer-based, and generative models, as well as building a multi-agent causal AI framework.

Key Responsibilities

• Architect and implement scalable ML systems integrating multi-modal omics data.
• Develop and deploy graph-based, transformer-based, and generative models including large language models (LLMs).
• Build a multi-agent causal AI framework utilizing causal graph learning and knowledge graphs.
• Collaborate with data engineering teams to design data pipelines for large-scale omics data.
• Implement, evaluate, and optimize causal inference methods such as directed acyclic graphs (DAGs) and treatment effects.
• Partner with experimental scientists to ensure outputs are interpretable and testable.

Required Skills & Qualifications

• Ph.D. or M.S. in Computer Science, Physics, Computational Biology, Biostatistics, or related field with 3+ years of experience.
• Proven experience in ML model development, particularly with transformers, graph networks, generative models, or causal inference.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, JAX, or PyTorch Geometric.
• Experience with multi-omics or high-dimensional biological data is preferred.
• Strong background in probabilistic modeling, causal reasoning, or statistical inference.
• Demonstrated ability to work in cross-disciplinary teams and deliver results in fast-moving environments.