Top Tech Jobs & Startup Jobs

Design and scale reinforcement learning post-training and distillation algorithms for simulation and autonomous driving. Develop reward models, realism critics, and discriminators for closed-loop multi-agent simulation. Conduct large-scale TPU ablation studies using driving behavior metrics to address simulator exploitation and distribution shift. Implement tested JAX code and collaborate with simulation, world modeling, and autonomous driving researchers and engineers.

Provides legal counsel for Waymo’s Canadian operations, advising on negotiations, advocacy, disputes, regulatory changes, compliance, governance, and business strategy. The role partners with public policy, regulatory, product, communications, and operations teams; engages Canadian regulators and authorities; supports global legal initiatives; and develops expertise in emerging Canadian legal issues affecting autonomous driving and ride-hailing services.

Lead growth marketing strategy for Waymo’s autonomous ride-hailing business, driving U.S. market launches, product adoption, use-case expansion, loyalty, customer engagement, and retention. Build and guide a high-performing team, develop segmentation and growth plans, collaborate cross-functionally, and establish measurement frameworks to evaluate investments and deliver measurable business outcomes.

Design and implement multimodal, multitask perception models for autonomous driving, including object detection, tracking, segmentation, occupancy detection, and foundation models. Train and evaluate machine learning models using large-scale Waymo driving data and infrastructure. Collaborate with technical teams while gaining experience in production-scale machine learning systems.

Lead Waymo’s Singapore go-to-market strategy and business operations. Responsibilities include market and competitive analysis, quantitative modeling, KPI development, cross-functional initiative leadership, international market scaling, stakeholder coordination, and engagement with government and strategic partners. The role serves as a bridge between Singapore and global teams, helps establish local processes, supports regulatory and policy considerations, and drives strategic decisions for commercial deployment.

Develop and productionize agentic systems that accelerate release qualification, improve developer velocity, and automate test development and execution. Work across frontend, backend, multiple programming languages, and large software systems while creating novel automation solutions. The role involves systems thinking, experimentation, and collaboration on robotics, firmware, hardware-in-the-loop, test engineering, release management, and AI-backed products.

Build and own production machine learning infrastructure for growth initiatives, including feature pipelines, point-in-time-correct training data, batch inference, model registries, retraining automation, validation safeguards, and production monitoring. Partner closely with data science and data engineering to productionize models, prevent data leakage and train-serve skew, and ensure reliable model-driven promotions.

Prepare month‑end and quarter‑end close deliverables, financial statements, flux analysis, and reporting packages; design operational processes and controls; lead automations and systems implementations; perform technical accounting research and documentation; collaborate with FP&A, Legal, AP, and Systems teams.

Develop high-performance GPU primitives, abstractions, profiling and debugging tools for autonomous vehicle compute platforms. Collaborate on GPU-focused SoC projects, bring up and optimize onboard software across new GPU platforms, and improve GPU testing and CI/CD infrastructure. The role requires strong C++ and CUDA expertise, GPU software and hardware knowledge, and performance analysis skills.

Develop controllable, realistic adversarial agents for simulation-based autonomous-driving evaluation and deploy them in the Waymo Simulator. Improve post-training recipes for foundation and world models. The internship requires pursuing a PhD in computer science, machine learning, robotics, or a related field, with proficiency in Python, tensor manipulation, machine learning, and deep learning. Experience with reinforcement learning, diffusion or autoregressive architectures, attention mechanisms, autonomous driving, or robotics is preferred.