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Harness the power of transformer architecture, a cutting-edge deep learning model widely employed in natural language processing and computer vision, to optimize the language model's performance and efficiency.
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Exploiting the transformative capabilities of transformer architectures to seamlessly process and reshape vast volumes of data, empowering the language model to achieve unprecedented levels of accuracy and versatility.
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Because of our investments in public cloud infrastructure and machine learning platforms, we are now uniquely positioned to harness the power of AI. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure.
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Background in machine learning with experience in large scale training and deployment of deep neural nets and/or transformer architectures. Experience with machine learning frameworks such as TensorFlow or Pytorch, Lightning, Mosaic ML etc.
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This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). We are looking for an experienced Sr. Lead Engineer, Generative AI Infrastructure to help us build the foundations of our AI capabilities.
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Build infrastructure for deploying search indexes and embeddings in vector databases that will work closely with the rest of our capabilities. Design and build run-time infrastructure for serving large ML models such as LLMs and FMs in our public cloud.
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Wire and solder wiring harness and cable assemblies, PC boards transformer assemblies, control consoles, connectors, etc. About FETFET (Forum Energy Technologies, Inc.) is a global company, serving the crude oil, natural gas, and renewable energy industries.
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You will work on a wide range of initiatives, whether that’s building large-scale distributed training clusters, or deploying LLMs on GPU instances for real-time applications and decisioning systems, or supporting cutting-edge AI research and development, all in our public cloud infrastructure.
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New York City (Hybrid On-Site): $201,400 - $229,900 for Lead Machine Learning EngineerSan Francisco, California (Hybrid On-Site): $213,400 - $243,500 for Lead Machine Learning EngineerRemote (Regardless of Location): $170,700 - $194,800 for Lead Machine Learning Engineer.
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New York City (Hybrid On-Site): $234,700 - $267,900 for Sr. Lead Machine Learning EngineerSan Francisco, California (Hybrid On-Site): $248,700 - $283,800 for Sr. Lead Machine Learning EngineerRemote (Regardless of Location): $198,900 - $227,000 for Sr. Lead Machine Learning Engineer.
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SummaryLocation: New York, NY; US Remote; Cambridge, MA; Chicago, IL; Atlanta, GA; Plano, TX; San Francisco, CA; McLean, VA; San Jose, CA; Wilmington, DEType: Full time. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC.
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Hands on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer) This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI.
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Every day, our team breaks new ground, as we build transformational AI technology and products for enterprise and developers to harness the power of LLMs. Cohere was founded by three global leaders in AI development, including our CEO, Aidan Gomez, who co-created the Transformer, which makes LLMs possible.
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Your contributions will be instrumental in shaping cutting-edge architectures, frameworks, and methodologies, pushing the boundaries of natural language processing (NLP) and machine learning. Design and prototype reusable components for LLM based solution patterns.
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Master's or Doctoral degree in Computer science, Computer Engineering, Electrical engineering, Mathematics, or a similar field. At least 8 years of experience programming with Python, Go, Scala, or Java.
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