Research Engineer, AI/ML Systems
New
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Lightning AIAI/ML Platform
London, England, United Kingdom; New York, New York, United States; Remote; San Francisco, California, United States; Seattle, Washington, United StatesFull-TimeMiddle
Salary$165,000 — $310,000 USD
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Job Details
- Required Skills
- Machine LearningPyTorchSoftware EngineeringDeep LearningGenerative AIDistributed Systems
Requirements
- Experience building, training, evaluating, or experimenting with deep learning models.
- Hands-on experience with deep learning frameworks such as PyTorch.
- Strong software engineering fundamentals building software and debugging and problem-solving skills, with the ability to investigate unfamiliar technical challenges.
- Curiosity, initiative, and a demonstrated ability to quickly learn new technologies and technical domains.
- Excellent communication and collaboration skills, including the ability to work effectively across research, product, infrastructure, and customer-facing engagements.
- Comfortable working in fast-moving, ambiguous environments where priorities evolve over time.
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Responsibilities
- Develop and post-train models, while building and improving the systems and workflows needed to run, evaluate, debug, and scale training workloads.
- Build software, tooling, and platform capabilities that improve how researchers, developers, and customers develop, train, and deploy AI systems.
- Contribute to Lightning’s open-source projects by building new features, improving existing functionality, and collaborating with the broader developer community.
- Work across deep learning systems, developer tooling, backend services, and platform infrastructure to solve a wide variety of engineering challenges.
- Collaborate directly with customers to understand real-world AI workloads, investigate technical challenges, and translate those learnings into reusable product and platform improvements.
- Prototype new ideas, evaluate approaches, and turn successful experiments into production-quality software.
- Partner closely with research, product, and infrastructure engineering teams to improve developer experience, AI workflows, and platform capabilities.
- Debug complex technical problems spanning machine learning, distributed systems, backend software, and developer tooling.
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