Hive, a leading provider of cloud-based AI solutions, is seeking a Staff Machine Learning Engineer to join their innovative team. With over $120M in funding from prestigious investors and a global presence across San Francisco, Seattle, and Delhi, Hive has established itself as a trusted partner for hundreds of major organizations in content moderation, brand protection, and AI-powered solutions.
The role demands a seasoned professional with 8+ years of experience who can excel in developing and deploying machine learning models at scale. You'll be working with terabyte-scale datasets and state-of-the-art neural networks, while taking ownership of projects from conception to production. The position offers a competitive salary range of $200,000 - $300,000, plus equity opportunities and comprehensive benefits.
As a Staff Machine Learning Engineer, you'll be responsible for the entire ML lifecycle - from designing and coding neural networks to deployment and continuous improvement. You'll lead cross-functional teams, mentor other engineers, and contribute to the strategic direction of the company. The ideal candidate combines technical expertise in frameworks like PyTorch or TensorFlow with strong leadership and communication skills.
The role offers an opportunity to work on cutting-edge AI technology while making a direct impact on one of the fastest-growing AI startups. You'll be part of a team that values innovation, ownership, and rapid development. The position includes comprehensive benefits such as health, dental, and vision insurance, gym membership, paid vacation, and potential equity grants.
Working at Hive means joining a team of ambitious individuals passionate about creating a revolutionary AI company. The steep learning curve and direct impact on company development make this an exciting opportunity for someone looking to shape the future of AI technology. The company's culture emphasizes continuous learning, innovation, and maintaining high engineering standards while moving quickly to deliver solutions at scale.