Top AI & Machine Learning Jobs

Lead architecture and delivery of perception and spatial AI models from cloud training to deployment on automotive-grade SoCs. Define deployment-first architectures, productionize state-of-the-art perception research (BEV/multi-camera), set hardware-aware validation metrics, optimize models for latency/memory/throughput, and build and mentor a high-performing engineering team while partnering with research, product, and customers.

Build and integrate agentic AI capabilities enabling LLM-driven real-world actions via location intelligence. Design execution logic, integrate data providers and services, implement tool interfaces, manage sessions and context, and rapidly prototype and iterate on reliable, cost-aware agentic workflows.

Build and own AI platform and orchestration systems for computer vision and multimodal products. Implement pipelines, artifact handoff, deployment patterns, job tracking, observability, and APIs. Enable cloud-based inference, storage, reproducibility, and operational reliability while partnering with data scientists to scale and productionize ML workloads.

Develop machine-learning and statistical solutions to measure and improve map and location quality. Design experiments and sampling strategies, define statistical quality metrics, build scalable data and computing tools, collaborate cross-functionally, and communicate results through presentations and white papers.

Build and own core simulation and geospatial systems supporting AI/perception and mapping products. Develop pipelines for scene construction, actor behavior, sensor and condition generation, scenario configuration, and tooling for reproducible, production-quality simulation outputs. Troubleshoot fidelity and stability, partner with data scientists and platform engineers, and contribute to roadmap and operationalization.

Lead design, development, and deployment of production ML systems for HD mapping, ADAS and IVI. Own end-to-end ML architecture, data ingestion, training, inference pipelines, MLOps (CI/CD, versioning, monitoring), and integrate outputs into map compilation. Drive models in computer vision, NLP, generative/agentic AI, and ensure scalable, reliable in-cloud or in-vehicle deployments while mentoring teams and enforcing engineering best practices.

Lead design and deliver efficient perception models for road scenes, optimize models for edge devices (Jetson, Snapdragon, ARM), improve real-time inference via quantization/pruning/distillation, convert/deploy with TensorRT/ONNX, build scalable training/evaluation with PyTorch or TensorFlow, and collaborate with embedded and software teams to produce production-ready edge AI systems.

Lead development of map-grounded world foundation models and generative scenario synthesis for AVs. Own end-to-end ML lifecycle, bridge generative models with classical simulators, define synthetic data quality and validation frameworks, run POCs with partners, and enable sim-to-real strategies to improve perception and planning.

Lead strategy and execution for generative and multimodal vision systems, from research to production. Design model architectures, adaptation, evaluation, and deployment pipelines; improve realism, controllability, robustness, and temporal consistency; benchmark and guide model selection; mentor scientists and coordinate cross-functional engineering to ship production-ready AI capabilities.

Lead design and implementation of map and location quality testing: develop ML and statistical test designs, sampling plans, metrics, data pipelines, and tools; collaborate with stakeholders and present results to internal and external customers.