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Metro location, mid-level role, and recognizable funded startup increase applicant competition.
Advanced ML/LLM skills are transferable across industries but require domain adaptation.
Explicit 5+ years and mandatory LLM/PEFT and production ML skills make filters strict.
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Lead development and fine-tuning of large-scale AI models (LLMs, transformers, diffusion models) for domain-specific applications.
Own model design, evaluation, bias/fairness checks, and performance optimization to deliver scalable AI solutions.
Lead and mentor a team of applied scientists and ML engineers, collaborating cross-functionally to deploy AI in production environments.
Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or related field.
5+ years hands-on experience in applied machine learning and data science.
Proven expertise in training and fine-tuning large-scale models using PyTorch, TensorFlow, Hugging Face, and PEFT methods (LoRA, prefix tuning, adapters).
Experience with MLOps practices, large datasets, feature engineering, and cloud/HPC ML workload scaling.
Deep technical expertise in foundation model adaptation and applied AI across NLP, computer vision, or multimodal systems.
Experienced leader capable of mentoring technical teams and translating cutting-edge research into practical business solutions.
Strong collaborator with product, engineering, and business stakeholders to deliver measured AI impact in production contexts.