Staff AI Engineer (NY)
$175,000–$287,000 year
HybridNew York City, New York, United States or New York, United States
Job Summary
Own end-to-end machine learning systems running at LinkedIn scale, specifically the recommender and classification systems powering core products. Translate product requirements into system design, train models, drive efficacy experiments, and manage GPU fleets for millisecond latency. Lead a significant AI workstream, align technical direction across orgs, and mentor engineers while delivering measurable member and business impact. Operate systems reliably as on-call, root-cause significant production regressions, and ship model-based systems with quantified value. Drive efficiency improvements in inference, training, or tech-debt backed by data.
Required Qualifications
- Bachelor's degree in Computer Science or related technical field or equivalent practical experience
- 4+ years of industry experience in software design, development, and algorithm related solutions
- 4+ years experience in programming languages such as Java, Python, etc.
- 4+ years experience with machine learning, data mining, and information retrieval or natural language processing
- This role will be based in New York City
- The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team
Desired Qualifications
- 6+ years of relevant AI/Machine Learning experience
- MS or PhD in Computer Science or related technical discipline
- Experience leading a significant project of 3+ AI engineers
- Experience with cross-functional communication to product, engineering, business or data science partners
- Experience with PyTorch or similar Deep Learning frameworks
- Experience with Spark for data manipulation and transformation
- Experience with A/B testing at scale in a consumer-facing product
- Experience with AI code development (i.e ClaudeCode, Codex, Copilot)
- Experience adapting pre-trained LLMs to production systems including fine-tuning and student-teacher model paradigms
- Experience applying AI/ML to recommender systems at scale
- Published work in academic conferences or industry circles
- Experience leading engineers to tackle a large-scale AI problem
- Strong technical background & Strategic thinking
- Experience in Machine Learning, Big Data and Deep Learning
- Experience in GAI and/or LLMs
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