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Senior, niche ML-infrastructure role reduces pool despite metro location and known Series-C startup brand.
Strong ML-infrastructure, GPU and backend systems focus gives high cross-industry background sensitivity.
Explicit 10+ years requirement, principal-level architecture ownership and specific ML/GPU infra experience make filters highly strict.
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Own architecture and technical direction for real-time data processing and high-throughput distributed messaging systems at scale.
Lead development and scaling of ML training and inference infrastructure including GPU capacity planning, scheduling, and optimization of latency and cost per request.
Drive system reliability, observability, and incident response for production systems serving enterprise customers; mentor senior engineers and set engineering standards.
10+ years experience in backend and infrastructure systems with proven architecture ownership at scale.
Hands-on experience with large-scale databases, high-throughput messaging systems, and real-time job queues.
Strong written communication skills for technical and business audiences across time zones.
Degree in Computer Science (BTech/MTech/PhD) or equivalent; track record prioritized over pedigree.
Experienced in navigating and reasoning about large, complex codebases and architectural tradeoffs in legacy systems.
Skilled in mentoring senior engineers and influencing technical decisions without direct authority.
Has background or interest in ML infrastructure, GPU scaling, model inference optimization, and possibly speech, NLP, or information retrieval systems.