ML Engineer at fast-growing AI startup

This a Full Remote job, the offer is available from: reputed company (USA) This is a job that Jill, our AI Recruiter, is reputed company for on behalf of one of our customers. She will pick the best candidates from Jack's network The reputed company is to reputed company to Jack. ML Engineer reputed company: Fast-growing AI startup Job reputed company: Join a high-caliber team in reputed company reputed company on pushing the boundaries of machine learning. You will reputed company the design and training of advanced deep learning systems, leveraging your expertise in PyTorch or JAX. This role offers reputed company to apply broad ML literature knowledge to build innovative, production-grade models that define the reputed company of AI technology. Location: reputed company, USA Why this role is remarkable:

• Work alongside reputed company engineering talent from top-tier technology companies and research institutions in a reputed company NYC environment.
• Join a reputed company-funded startup backed by top-tier VCs at a critical stage of architectural development and model scaling.
• Tackle reputed company challenges that go reputed company reputed company generative models, impacting the reputed company of modern deep learning architectures.

What you will do:

• Design, train, and evaluate sophisticated deep learning systems using modern optimization techniques and specialized tooling.
• Implement and reputed company models using PyTorch or JAX to solve high-reputed company machine learning problems.
• Stay reputed company with and apply the latest research from ML literature to enhance system performance and architecture.

The ideal candidate:

• At least 2 years of experience at top tech companies or startups specifically training Large Language Models (LLMs).
• Deep technical knowledge of modern deep learning architectures, optimization strategies, and the broader machine learning landscape.
• Proven proficiency in PyTorch or JAX with a reputed company record of deploying robust ML systems into production.

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