What we need to see: PhD/MSc in CS, CE, Data Science, Statistics, or a related field (or equivalent experience). 5+ years in applied ML/AI or large-scale systems, with 2+ years building agentic or LLM-powered applications in production environments. Proficiency in Python; hands-on experience with agentic frameworks such as LangChain, LangGraph, CrewAI, or similar. Hands-on fine-tuning experience across full-weight, LoRA, and PEFT methods, with a solid grasp of key concepts including scaling laws and advanced alignment techniques (RL, RLHF, RLAIF, KL-divergence regularization, and related methods). Hands-on NLP experience before the LLM era includes BERT, word embeddings, sequence models, and classical NLP pipelines. You understand what is under the hood and see through the abstraction. Solid ML foundations: supervised learning, feature engineering, model evaluation, and statistical modeling. Strong communication skills — able to align diverse collaborators, document assumptions clearly, and drive progress without formal authority. Ways to stand out from the crowd: Experience in networking or NW silicon domains — familiarity with network architectures, switch/NIC build flows, or related engineering concepts. Strong software engineering fundamentals with a track record of shipping production systems. Data engineering background: SQL/NoSQL, data pipelines, and working with large-scale structured and unstructured datasets. Ability to cut through complexity — knowing what matters, drilling into the right details, and keeping the business goal in sight. We are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to
