Track record of shipping machine learning models that delivered measurable impact.

Ability to work independently on well-scoped problems and contribute meaningfully to larger systems.

Strong theoretical foundation in machine learning, including the mathematical underpinnings of predictive modeling, optimization, probability, and linear algebra.

Demonstrated experience with representation learning techniques (embeddings, self-supervised learning, autoencoders, contrastive learning, or related approaches).

Proven track record of building predictive models (regression, classification, structured prediction) on real-world data, with rigorous evaluation practices.

Hands-on experience implementing machine learning systems in Python, with proficiency in frameworks such as PyTorch, TensorFlow, or JAX, alongside the standard data stack (NumPy, pandas, scikit-learn).

Ability to read, critically evaluate, and implement ideas from research papers.

Solid software engineering fundamentals: version control, testing, code review, debugging, and performance profiling.

Experience taking models from prototype to production, including deployment patterns, monitoring, and managing distribution shift.

Excellent English communication skills (written and verbal) are a big plus. The candidate is expected to discuss complex technical concepts clearly, produce high-quality documentation, and collaborate effectively with an English-speaking team.

Exposure to biology, computational biology, bioinformatics, or related life sciences (through prior work, research, or coursework) is a plus but not required.

Familiarity with biological data modalities such as sequencing data, omics, protein and molecular structure, or biomedical imaging.

Publications, open-source contributions, or technical blog posts demonstrating depth in a machine learning specialization.

Experience with distributed training, model optimization, or large-scale inference.

Familiarity with MLOps tooling (e.g., MLflow, Weights & Biases, Kubeflow).