Machine Learning Engineer

New

Skills

Causal Inference Generative Models Graph Networks JAX Machine Learning Multi-omics Data Python PyTorch TensorFlow Transformers

We are seeking a skilled (Senior) Machine Learning Engineer to architect and implement scalable machine learning systems that integrate multi-modal omics data. The ideal candidate will be responsible for developing and deploying advanced models including graph-based, transformer-based, and generative models such as large language models (LLMs).

Key Responsibilities
  • Architect and implement scalable ML systems that integrate multi-modal omics data.
  • Develop and deploy graph-based, transformer-based, and generative models including LLMs.
  • Build a multi-agent causal AI framework with causal graph learning and knowledge graphs.
  • Collaborate with data engineering to design pipelines for large-scale omics data.
  • Implement, evaluate, and optimize causal inference methods (DAGs, treatment effects).
  • Partner with experimental scientists to ensure outputs are interpretable and testable.
Required Skills & Qualifications
  • Ph.D. or M.S. in CS, Physics, Computational Biology, Biostatistics, or a related field with 3+ years of experience.
  • Proven ML model development experience 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.

No forms. Your profile is generated instantly.

Job Type: Remote

Salary: Not Disclosed

Experience: Entry

Duration: Months

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