Lead ML Engineer to provide technical leadership for the development and industrialisation of Scores & Attributes across multiple international markets, working at the intersection of machine learning, data engineering and platform engineering.
Responsibilities
Lead the design and evolution of capabilities for developing, calculating, testing, governing, deploying and operating data attributes at scale.
Build reusable frameworks, tooling and automation for attributes to move from experimentation to production-ready services.
Shape the end-to-end technical architecture and engineering patterns supporting the attribute lifecycle.
Design integration patterns allowing attribute services to work with existing data and bureau platforms.
Evaluate new data sources and methodologies, quantifying their incremental predictive and analytical value.
Requirements
Nice to have: Apigee
Nice to have: Okta
Nice to have: Migrating legacy analytical/data workloads onto modern cloud platforms
Nice to have: Developing platforms, frameworks or tooling used by Data Science/Analytics teams
Nice to have: Experience in regulated, data-intensive environments such as financial services
Personal Development - career pathway for professional growth supported by learning and development programs and unlimited access to online educational training courses, learning materials and books
Design, build, and improve AI agent evaluation systems within NVIDIA’s NeMo Platform.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
Responsibilities
Design and implement Python-first APIs, SDK workflows, and plugin interfaces for building, measuring, and improving agents across multiple runtimes and product surfaces
Build reusable systems for observing behavior, measuring progress, detecting regressions, and turning runtime evidence into product decisions
Build systems for ingesting, normalizing, validating, and analyzing agent execution data and evaluation datasets
Partner with research, product, platform, and infrastructure teams to integrate agentic capabilities broadly across NVIDIA agent runtimes and developer workflows
Help turn emerging agent development and improvement techniques into reliable, reusable product capabilities
Requirements
BS, MS, or equivalent experience in Computer Science, Computer Engineering, or a related technical field
Nice to have: evaluation workflows for heterogeneous agents
Design and implement Python-first APIs, SDK workflows, and plugin interfaces for building, measuring, and improving agents across multiple runtimes and product surfaces; build reusable systems for observing behavior, measuring progress, detecting regressions, and turning runtime evidence into product decisions; partner with research, product, platform, and infrastructure teams to integrate agentic capabilities broadly across NVIDIA agent runtimes and developer workflows; provide senior technical leadership through design reviews, code reviews, mentoring, and ownership of ambiguous cross-component problems.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
Responsibilities
Design and implement Python-first APIs, SDK workflows, and plugin interfaces
Build reusable systems for observing behavior, measuring progress, detecting regressions
Build systems for ingesting, normalizing, validating, and analyzing agent execution data and evaluation datasets
Partner with research, product, platform, and infrastructure teams to integrate agentic capabilities
Help turn emerging agent development and improvement techniques into reliable, reusable product capabilities
Requirements
BS, MS, or equivalent experience in Computer Science, Computer Engineering, or a related technical field
Nice to have: Experience building, deploying, and iterating on production agentic AI systems where evaluation was used to measure and improve real product outcomes
Nice to have: Experience designing evaluation workflows for heterogeneous agents, including tool-using agents, RAG agents, workflow agents, coding agents, or long-running autonomous systems
Nice to have: Experience integrating evaluation capabilities across multiple products, runtimes, or internal platforms, especially through Python SDKs, plugins, or shared developer tooling
Nice to have: Strong ability to connect technical evaluation work to business outcomes, product quality, user experience, reliability, or operational efficiency
