Staff Applied AI Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Design and implement a comprehensive machine learning and agentic architecture, which encompasses model hosting and serving, prompt and model versioning, retrieval and embeddings, agent tooling, guardrails, and evaluation.

• Evaluate and select between deterministic, large language model, and agent-based methodologies, considering trade-offs related to accuracy, latency, cost, and reliability.

• Create evaluations that link offline and online quality metrics to business outcomes and risk management controls.

• Take ownership of experimentation, versioning, CI/CD processes for models and prompts, as well as monitoring, drift detection, rollback, and incident preparedness.

• Define deployment strategies and proactively identify potential failure modes prior to launch.

• Integrate safety guardrails, hallucination mitigation, bias testing, and sensitive-data handling into the system architecture.

• Specify requirements for AI-ready training and retrieval data, labeling, feature availability, and vector/search infrastructure.

• Collaborate with data engineering and platform teams to establish governed infrastructure.

• Convert ambiguous objectives into technical initiatives, complete with hypotheses and success criteria.

• Prioritize and sequence a portfolio of AI projects while developing clear execution paths.

• Provide guidance to product and engineering leadership on feasibility, cost, risk, and anticipated returns.

• Develop reusable architectural frameworks and delivery patterns.

• Mentor seasoned individual contributors on applied AI implementation and production quality standards.

• Work closely with a product engineering team on customer-centric capabilities.

• Create predictive ordering models, agentic workflow copilots, and infrastructure for evaluation and operations.

⛳️ Requirements

• A minimum of 10 years in applied data science, machine learning, or applied AI, with a proven track record of delivering production systems that impacted business metrics.

• Demonstrated ownership of AI and machine learning system architecture, covering serving, retrieval, evaluation, guardrails, and operational workflows.

• Extensive experience with large language models and agent technologies, including evaluation, failure analysis, and the judicious selection of deterministic methods when necessary.

• Experience in machine learning operations, including versioning, CI/CD for models and prompts, monitoring, drift detection, and rollback processes.

• Portfolio-level accountability for prioritizing competing AI initiatives and establishing execution paths.

• Significant daily engagement with AI-native engineering workflows in design, coding, debugging, and review over a period of at least 18 months.

• Experience in establishing model governance, monitoring protocols, and responsible AI standards for a team.

• Proficiency in implementing solutions across at least two cloud or technical ecosystems, such as AWS and GCP.

• Strong quantitative skills in experimentation, statistical reasoning, and causal analysis.

• Ability to align stakeholders from product, engineering, and operations on sequencing and trade-off decisions.

• Preferred: experience with retrieval systems, vector search, ranking, recommendation systems, or personalization in production environments.

• Preferred: experience with self-hosted or local AI infrastructures, including self-managed agent environments.

• Preferred: background in e-commerce, B2B procurement, vendor management, financial products, or integration with external systems.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Opportunities for professional development and continuous learning.

• Flexible working hours and remote work options.

• Comprehensive health, dental, and vision insurance.

• Generous vacation and leave policies.

• Collaborative and innovative work environment.