Top AI & Machine Learning Jobs

Lead development and deployment of AI/ML solutions using telematics and large-scale data. Build and maintain data pipelines, prototype generative and agentic AI, train and validate models, guide data scientists, perform statistical analysis and reporting, and implement MLOps in cloud environments to surface vehicle safety insights.

Build and scale the ML compute backend: develop core backend services, integrate with GPU hardware and orchestration systems, optimize GPU utilization and system performance, ensure reliability and scalability, work with cloud providers and Kubernetes at scale, and lead large cross-team initiatives to improve ML platform usability and efficiency.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

As an ML Infrastructure Engineer, design backend components, enhance platform performance, and drive system improvements while collaborating across teams.

Develop and maintain large-scale evaluation, introspection, and visualization tools for autonomous vehicle ML models. Build metrics, dashboards, and pipelines that process billions of examples, collaborate with data, infra, simulation, and modeling teams, and provide technical leadership to align offline evaluation with real-world AV behavior.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

Develop and implement validation methods, tools, and production code to analyze autonomous vehicle behavior using simulation and test data. Build scalable testing products, generate data-driven insights for AI and systems engineers, and establish validation best practices across teams.

Lead development and productionization of advanced forecasting and predictive models: assess existing analytics, define data needs, design methodologies, run proofs-of-concept, architect cloud-based model deployments, and monitor model performance while translating results for business stakeholders.

The Senior Systems Data Scientist will develop methodologies for measuring trust in AV testing, manage data analysis frameworks, collaborate with teams, and provide insights for decision-making.

Design and deliver enterprise-grade AI workflow transformations on GCP. Build secure, event-driven backend services and multi-step AI agents, integrate bots with enterprise systems, implement telemetry, testing, CI/CD, HITL validation, and create reusable architecture, documentation, and runbooks while guiding adoption and governance.