Sr. Lead AI Engineer (AI Foundations)
$229,900–$262,400 year
On-siteNew York City, New York, United States
Job Summary
Partner with cross-functional teams to deliver AI-powered products that enhance customer experiences and associate workflows. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, and observability. Leverage a broad stack of Open Source and SaaS technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and Nemo Guardrails. Invent and introduce state-of-the-art LLM optimization techniques to improve scalability, cost, latency, and throughput of production systems. Contribute to the technical vision and long-term roadmap of foundational AI systems. This role sits within the Intelligent Foundations and Experiences team, advancing state-of-the-art science and engineering to deliver scalable, high-performance AI infrastructure.
Required Qualifications
- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
- At least 6 years of experience developing AI and ML algorithms or technologies
- Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
- At least 4 years of experience developing AI and ML algorithms or technologies
- At least 6 years of experience programming with Python, Go, Scala, or Java
Desired Qualifications
- 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
- Experience designing, developing, integrating, delivering, and supporting complex AI systems
- Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders
- Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang
- Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
- Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production
- Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
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