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Remote role, metro locations, and mid-level seniority increase applicant competition to medium.
Niche foundation-model and distributed-inference requirements create high domain bias, limiting cross-industry transferability.
PhD or equivalent, deep foundation-model expertise, and mandatory distributed-inference skills make filters highly strict.
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Lead design and evaluation of advanced neural architectures including large language models, hybrid Transformer-SSM, diffusion, and mixture-of-experts models.
Develop scalable synthetic data pipelines and model-generated training approaches to improve model alignment, efficiency, and quality.
Drive distributed neural-network inference methods across heterogeneous hardware and environments, improving latency, energy use, privacy, and resiliency.
PhD in computer science, machine learning, AI, applied mathematics, or related field, or equivalent research/industry experience.
5+ years of relevant research or advanced development experience with modern foundation models or generative AI.
Deep expertise in at least two areas among LLM architecture, state-space models, hybrid neural architectures, diffusion/flow models, synthetic-data training, model optimization, or distributed inference.
Experience with distributed training/inference systems, model parallelism, and familiarity with Python plus frameworks like PyTorch or JAX.
Demonstrated ability to convert advanced research concepts into practical, production-relevant implementations with strong evaluation rigor.
Experience leading ambiguous research projects and collaborating across research and engineering teams to influence product roadmaps.
Technical strength in mathematical foundations of deep learning and hands-on skills in system-level optimizations for modern AI infrastructures.