Machine Learning Operations Engineer

August 28

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Earth Species Project

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Description

• Oversee ESP's ML infrastructure, maximizing performance • Work closely with machine learning scientists and biologist/ecologist partners to support the implementation of ML models in the study of non-human communication • Build and manage data scraping and processing pipelines for large-scale machine learning model training • Conduct R&D to retrain, optimize, scale-up, and deploy ML models, converting them to portable formats like ONNX • Maintain a unified code base for data processing, ML training, and analysis tools • Translate complex research into practical demo applications for bioacoustic data analysis • Stay updated with the latest advancements in machine learning and explore their applications within the organization

Requirements

• Advanced degree in Computer Science, Data Science, Machine Learning, or a related field • Co-authored publications at top AI conferences such as NeurIPS, ICML, ICLR, AAAI • Experience with training large ML models on large datasets, including data and model parallelization and foundation models • Strong experience in infrastructure building and software development using Python • Proven experience in developing large-scale data collection and processing pipelines • Expertise in deploying ML and deep learning models at scale • Familiarity with major ML frameworks such as PyTorch, TensorFlow, and/or JAX • Experience with cloud infrastructure providers like GCP, Azure, AWS • Strong problem-solving abilities and creativity • Excellent communication skills, capable of explaining complex concepts to non-technical audiences • Experience in a fast-growing startup environment with leadership potential

Benefits

• $155,000-185,000 USD depending on the level of seniority • Medical insurance, dental insurance, and vision insurance - ESP covers 100% of the premium • 401k plan with match (if based in the United States) • 2,000 USD home office stipend • Unlimited paid time off, with a recommended minimum of three weeks per year • Flexible working hours • Regular team retreats around the world