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Machine Learning Engineer at Output | New York City

OutputNew York HQ 🗽
On-site Full-time

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Experience Level

Experience

Qualifications

Desired Qualifications:Bachelor's degree in Computer Science, Machine Learning, or a related technical discipline. Over 3 years of experience in developing and implementing deep generative learning models. Experience with model pre-training and proficiency in distributed computing environments. Strong programming skills in Python and expertise in at least one major deep learning framework (PyTorch, TensorFlow, or JAX). Familiarity with deep learning and generative architectures, including transformers, diffusion models, and autoencoders. Experience in handling terra-scale datasets and scaling models to billions of parameters. Solid understanding of machine learning fundamentals, encompassing various model architectures, optimization techniques, and evaluation metrics.

About the job

Join Our Innovative Team

Become a pivotal member of our team dedicated to creating the world's first biological reasoning model. Collaborate with us on groundbreaking generative foundational models that interpret biological systems across various scales, from molecular interactions to entire organisms, empowering us to predict, comprehend, and manipulate living systems like never before.

Output is currently in stealth mode, driven by a team of seasoned founders and biotech experts who have successfully exited ventures in AI and biotechnology, supported by leading venture capital firms including Y Combinator.

As a Machine Learning Engineer, you will partner with our founders and talented team members to innovate and deploy advanced AI solutions that facilitate intricate biological reasoning across diverse scales.

  • Develop foundational models for biological systems capable of encoding and decoding biological data at scale.

  • Create deep generative models tailored for biological applications, experimenting with innovative architectures to capture the complexities of multi-scale biological systems.

  • Work on distributed training systems to scale our models to billions of parameters, focusing on performance and efficiency across multi-GPU and multi-node configurations while processing extensive biological datasets.

  • Design efficient data pipelines to manage and process large biological datasets, tackling challenges in data loading, partitioning, and memory optimization.

  • Establish and implement robust evaluation frameworks for complex biological models, ensuring data integrity and preventing leakage across dataset splits.

About Output

At Output, we are pioneering the development of a unique biological reasoning model. Our team consists of experienced founders and industry veterans in biotech and AI, working in a stealth mode to disrupt traditional approaches with innovative technologies. With backing from prestigious investors like Y Combinator, we are committed to revolutionizing the intersection of AI and biology.

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