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Lila Sciences

Machine Learning Engineer, Biomolecule Design

Lila Sciences, Cambridge, Massachusetts, us, 02140

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Machine Learning Engineer, Biomolecule Design Overview Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducingscientific superintelligence to solve humankind's greatestchallenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.

Responsibilities

Training and deploying the next generation of state-of-the-art models for biomolecule (DNA, RNA, and proteins) design.

Scientific reinforcement learning environments to improve model performance.

Autonomous pipelines that integrate experimental feedback with in silico predictions.

Qualifications

Proficiency with training, testing, and deploying models.

Experience with machine learning frameworks (PyTorch, JAX, etc).

Experience with ML models in biological domains.

Strong software engineering skills and ability to rapidly develop working prototypes.

Motivated by code quality, performance, and design.

Passionate about the impact of AI for science.

Experience with training and using state-of-the-art biomolecule design models (AlphaFold, Evo2, ESM, etc).

Experience with Kubernetes.

Experience with distributed systems or high-performance computing.

Publications or collaborations in ML for biology.

Seniority level

Entry level

Employment type

Full-time

Job function

Science

Industries

Technology, Information and Internet

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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