About Ten Lives

Ten Lives (YC S22) is pioneering research in AI and biology to feed the world, sustainably. Our flagship brand decarbonizes the pet food industry by reducing the fifth of the world’s meat consumed by pets.

About the role

Skills: Torch/PyTorch, Python, TensorFlow

Ten Lives is seeking a skilled and motivated Machine Learning Engineer to tackle the problem of DNA sequence design. This position offers an opportunity to work at the forefront of generative modeling research, applying advances from natural language processing to the language of life - DNA. As an early employee, you will have the opportunity to shape our machine learning efforts and collaborate with our interdisciplinary team of computational and experimental scientists.

Responsibilities:

Drive the implementation, optimization, and deployment of state-of-the-art machine learning models for DNA sequence design.
Develop efficient, robust code and data pipelines, ensuring high-quality results.
Analyze and interpret computational approaches, presenting findings to colleagues in regular update meetings.
Establish automated processes to continually evaluate and enhance our design methodology.
Collaborate with diverse teams across biology and machine learning, contributing to the scientific and strategic vision of the company.

Qualifications:

Master's or PhD degree in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or related disciplines.
Demonstrated ability to reimplement and analyze state-of-the-art models.
Domain expertise in language models, variational autoencoders, and/or sensitivity analysis methods.
Proficiency in writing clean and efficient code and deploying services on cloud computing platforms like GCP, AWS, or Azure.
Interest in climate solutions and likes cats.

Preferences (optional):

Familiarity with fundamental biology.
Experience in developing novel machine learning methodologies.
Knowledge of state-of-the-art machine learning models for protein or DNA sequence design.
Publications in reputable machine learning conferences or scientific journals.

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