Senior AI Architect

Nexxa.AI•Sunnyvale, CA

1d•Hybrid

About The Position

We’re looking for a Senior AI Engineer who has spent the last 2+ years building real AI products at the frontier—someone who understands modern AI deeply and has turned that understanding into production systems used by customers. This role is ideal for candidates with experience at a frontier AI company, top research lab, or PhD‑level background who want their work to directly shape product and user experience rather than remain purely research‑driven. You will own AI‑powered features end‑to-end, working closely with product and engineering to bring cutting‑edge models into reliable, scalable, and user‑facing systems.

Requirements

5+ years of related professional experience in software engineering, machine learning, data science, or closely related technical roles
At least 2+ years of recent, hands‑on experience working deeply in modern AI
AI must be a primary responsibility, not a side project or minor component of the role
One or more of the following backgrounds: Senior‑level engineer or applied scientist at a frontier AI company
PhD in Machine Learning, AI, Computer Science, or a closely related field
Experience in a top academic or industrial AI lab, combined with strong product execution
Strong experience applying modern deep learning techniques (e.g., transformer‑based models) to real-world problems
Proven ability to ship AI‑powered features to production and maintain them over time
Excellent programming skills in Python, with experience in production ML frameworks (e.g., PyTorch, JAX)
Ability to independently scope ambiguous problems and deliver end‑to‑end AI solutions in collaboration with product and engineering teams

Nice To Haves

Publications at top venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR), or equivalent industry impact
Experience fine‑tuning, aligning, or evaluating large models (e.g., RLHF, preference modeling, eval harnesses)
Ability to reason deeply about model behavior, failure modes, and scaling tradeoffs

Responsibilities

Own and deliver AI‑driven product features end‑to-end, from problem definition to production rollout
Design AI systems that are deeply grounded in company data, using large‑scale datasets to create rich context for models
Partner with data engineering and analytics teams to: Leverage data warehouses and lake house architectures (e.g., Snowflake, BigQuery, Redshift, Databricks)
Define data models and pipelines that power AI use cases
Apply data science best practices to understand user behavior, system performance, and model impact
Build and maintain data‑driven context layers for AI systems, such as: Retrieval and ranking pipelines
Feature stores or embedding indices
Aggregations and transformations over large datasets
Translate raw, messy, large‑scale data into clean, structured inputs that improve model quality and reliability
Make pragmatic tradeoffs across data freshness, model performance, latency, and cost
Define evaluation, monitoring, and feedback loops using both offline analysis and production metrics
Collaborate closely with product managers to align AI capabilities with user needs and business goals
Iterate quickly: ship, measure impact, refine models and data pipelines, and repeat