Sr. Lead AI Engineer (AI Foundations)
$229,900–$262,400 year
On-siteNew York, United States
Job Summary
Partner with cross-functional teams to deliver AI-powered products that transform customer interactions and internal workflows. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, and model observability. Leverage a broad stack of Open Source and SaaS technologies such as AWS Ultraclusters, Huggingface, and PyTorch to build scalable solutions. Invent state-of-the-art LLM optimization techniques to improve scalability, cost, latency, and throughput for large-scale production systems. Contribute to the long-term technical vision and roadmap of foundational AI infrastructure. Demonstrate the ability to lead engineering teams, mentor others, and influence cross-functional stakeholders while applying novel research techniques to production environments.
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
- At least 6 years of experience programming with Python, Go, Scala, or Java
- 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
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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