Staff Applied AI Scientist

Responsibilities

• Own end-to-end AI and machine learning architecture, including model hosting and serving, prompt and model versioning, retrieval and embeddings, agent tooling, guardrails, and evaluation.
• Choose deterministic, large language model, or agent-based approaches based on accuracy, latency, cost, and reliability trade-offs.
• Build evaluation systems connecting offline and online quality to business outcomes and risk controls.
• Own experimentation, versioning, CI/CD for models and prompts, monitoring, drift detection, rollback, rollout strategy, and incident readiness.
• Implement safety guardrails, hallucination mitigation, bias testing, and sensitive-data handling.
• Define AI-ready data requirements, including training and retrieval data, labeling, feature availability, and vector or search infrastructure; partner with data engineering and platform teams to implement them.
• Prioritize AI opportunities, define hypotheses and success criteria, and sequence execution across a portfolio.
• Advise product and engineering leadership on feasibility, cost, risk, and expected return; establish reusable architecture and delivery patterns.
• Mentor experienced individual contributors on applied AI execution and production quality.
• Deliver predictive ordering, agentic workflow copilots, and evaluation and operations capabilities for procurement and finance workflows.

Requirements

• 10+ years of experience in applied data science, machine learning, or applied AI.
• Repeated delivery of production systems that measurably improved a business metric.
• Ownership of AI and machine learning system architecture, including serving, retrieval, evaluation, guardrails, and operational processes.
• Deep knowledge of current large language model and agent technologies, their failure modes, evaluation methods, and appropriate use cases.
• Experience with machine learning operations, including versioning, CI/CD for models and prompts, monitoring, drift detection, and rollback.
• Portfolio-level prioritization of competing AI opportunities and development of execution plans.
• At least 18 months of daily use of AI-native engineering workflows across design, coding, debugging, and review.
• Experience establishing model governance, monitoring, and responsible AI standards.
• Working implementation proficiency across at least two cloud or technical ecosystems, such as AWS and Google Cloud.
• Strong quantitative foundation in experimentation, statistical reasoning, and causal thinking.
• Ability to align product, engineering, and operations stakeholders on sequencing and trade-offs in ambiguous situations.

Preferred Qualifications

• Retrieval systems, vector search, ranking, recommendation, or production personalization experience.
• Self-hosted or local AI infrastructure, including self-managed agent environments.
• Experience in e-commerce, B2B procurement, vendor management, financial products, or integrations with external systems.

Success Measures

• Multiple AI capabilities are live in production with clear hypotheses and measured outcomes within the first 6–9 months.
• Established model architecture and evaluation approaches are adopted by other initiatives.
• Monitoring, drift detection, rollback, and incident playbooks operate reliably under real conditions.