Waabi

<div><span>Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.</span></div>
<div> </div>
<div><span>With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: </span><a href="http://www.waabi.ai" rel="noopener noreferrer ugc" target="_blank">www.waabi.ai</a></div><div><br /></div><div>
<p><span>As a Research Engineer focused on Generative AI Deployment, you will own the path from research to production for the neural and generative models that power Waabi World, our multi-sensor simulator. You will partner with research teams on models such as feed-forward scene reconstruction models, diffusion models, and vision foundation models. Your job is to make those models production ready: fast, memory-efficient, reproducible, and numerically trustworthy at the scale of closed-loop simulation. </span></p>
<br />
<p><span>You will…</span></p>
<ul>
<li>
<p><span>Be part of a multidisciplinary team of Research Scientists and Engineers building a best-in-class multi-sensor simulation stack.</span></p>
</li>
<li>
<p><strong><span>Take cutting-edge models from research to production.</span></strong><span> Turn research code and checkpoints into versioned, reproducible, exportable production models. These models run in closed-loop simulation next to GPU renderers and the autonomy stack, and in the pipelines that reconstruct 4D simulation scenes.</span></p>
</li>
<li>
<p><strong><span>Make generative models leaner and faster  to run.</span></strong><span> Profile end to end, from the model graph to GPU kernels and memory movement. Apply techniques like model compilation (e.g., TensorRT), mixed precision and quantization, batching, CUDA graphs, and custom kernels to achieve latency and memory budgets. For productionizing diffusion and flow-matching models, this can also include domain-specific techniques like faster sampling techniques and distillation.</span></p>
</li>
<li>
<p><strong><span>Own correctness as part of performance.</span></strong><span> Build benchmarks and numerical-parity tests focused on simulation realism and downstream autonomy metrics. These checks make sure that each speedup keeps simulation realism intact, and they catch regressions in CI.</span></p>
</li>
<li>
<p><strong><span>Build shared deployment tooling.</span></strong><span> Design the export, versioning, model registry, and build tooling that lets the sensor simulation team ship new models to simulation with confidence.</span></p>
</li>
<li>
<p><strong><span>Shape models for deployment.</span></strong><span> Work with research scientists early, so that new architectures are exportable, efficient, and hardware-friendly from the get-go.</span></p>
</li>
</ul>
<p> </p>
<p><span>Qualifications:</span></p>
<ul>
<li>
<p><strong><span>Shipping Production Software.</span></strong><span> You have experience reading and developing production-quality software at scale, not only prototypes. You have shipped ML systems that other engineers and researchers depend on every day.</span></p>
</li>
<li>
<p><strong><span>Model Optimization and Deployment.</span></strong><span> You have taken models from a research codebase to an optimized production artifact, and measured both the speedup and the accuracy change. You have hands-on experience with several of the following: TensorRT, torch.compile, torch.export, ONNX, post-training quantization, quantization-aware training, and pruning. You have handled models with custom CUDA operators or other operations that don't export cleanly.</span></p>
</li>
<li>
<p><strong><span>GPU Performance.</span></strong><span> You profile with tools such as Nsight Systems, Nsight Compute, and the PyTorch profiler. You reason about memory bandwidth, kernel launch overhead, and host-device synchronization. You can write or modify CUDA or Triton kernels.</span></p>
</li>
<li>
<p><strong><span>Modern ML Architectures.</span></strong><span> You can read research code for modern architectures, such as vision transformers, diffusion and flow-matching models, and vision foundation models, and see where the compute and memory go. You know which accuracy trade-offs matter when you lower precision, compile a model, or cut its sampling steps.</span></p>
</li>
<li>
<p><strong><span>Cloud Infrastructure.</span></strong><span> You have experience launching and debugging GPU jobs in the cloud (AWS, GCP, or equivalent). This includes job orchestration, containerized environments, and keeping CUDA, PyTorch, and TensorRT versions compatible across a fleet.</span></p>
</li>
</ul>
</div>

Senior / Staff Research Engineer, Generative AI Deployment

<div><span>Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.</span></div>
<div> </div>
<div><span>With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: </span><a href="http://www.waabi.ai" rel="noopener noreferrer ugc" target="_blank">www.waabi.ai</a></div><div><br /></div><div><strong><span>You will..</span></strong></div>
<ul>
<li>Build and evolve our training infrastructure on Kubernetes with Infrastructure — GPU scheduling, autoscaling, multi-node distributed jobs, capacity strategy, and the operators and workflow engines that keep long-running training reliable.</li>
<li>Shape the developer-facing surface — CLIs, SDKs, job submission, templates, paved paths — designed with the teams who'll use them. Make the common case one command and keep the uncommon case possible.</li>
<li>Shorten the inner loop. Time to first training run, edit-to-signal latency, local iteration before a job hits the cluster, fast failure over slow mystery. Measure it, publish it, drive it down.</li>
<li>Evangelize best-in-class tooling and frameworks. Track what the ecosystem is shipping, evaluate honestly, and make the case with working prototypes and migration paths — or say plainly when a shiny thing isn't worth the switching cost.</li>
<li>Strengthen the data and artifact layer. Dataset versioning, sharding, and high-throughput loading of large multimodal sensor data, so jobs saturate GPUs instead of waiting on I/O.</li>
<li>Turn one-off Python into durable tooling — tested, documented, observable libraries, CLIs, and services with sane defaults, and deletions where they're overdue.</li>
<li>Make experiments legible, with the teams who live in them: experiment hygiene, dashboards researchers trust, a real model registry, and lineage from dataset to checkpoint to simulation result.</li>
<li>Ship CI/CD for models alongside autonomy and simulation, so a model change is validated the same way a code change is.</li>
<li>Build observability across the ML stack — utilization, throughput, failure modes, queue times, cost per experiment. When a job fails at 3am on node 47, the researcher should find out why without you.</li>
<li>Treat docs, onboarding, and support as product surface — golden-path guides, a new researcher productive on day two, office hours that turn repeat questions into shipped fixes.</li>
<li>Drive adoption, not just availability. Prototype with real users, watch them work, iterate. A tool nobody adopts didn't ship.</li>
<li>Make the platform boringly reliable — fewer failures, faster recovery, and none of the manual steps that quietly cost a team days.</li>
<li>Build guardrails that don't feel like walls, with Security, IT, and Infrastructure: access controls, data handling, and cost governance that hold up in an IP-sensitive environment while staying self-serve.</li>
</ul>
<div> </div>
<div><strong>Qualifications:</strong></div>
<ul>
<li>5+ years of software or infrastructure engineering, including tools or platforms used by other engineers and operating ML or data-intensive production systems.</li>
<li>Hands-on Kubernetes expertise — GPU scheduling, autoscaling, Helm or equivalent, networking fundamentals, and the ability to debug a cluster under load rather than restart it.</li>
<li>Excellent Python, and a track record of designing APIs and CLIs other people enjoy using.<br />Practical AWS depth: object storage at scale, IAM, GPU compute, networking, cost management, and infrastructure as code (Terraform, Pulumi, or similar).</li>
<li>Distributed training in PyTorch (DDP, FSDP, or similar), plus experiment tracking and model registry tooling — from the perspective of someone who made them pleasant for others to use.</li>
<li>Fluency with containers, CI/CD, and modern build systems, including large monorepos.</li>
<li>The ability to influence without authority: evaluate a framework on its merits, pilot it credibly, and persuade skeptical senior engineers to change how they work.</li>
<li>A collaborative default — you'd rather co-own a system than draw a boundary around your part of it.</li>
<li>User empathy: you'd rather fix the third-most-interesting problem blocking ten people than the most interesting one blocking nobody.</li>
<li>Strong product instincts, strong writing, and comfort operating autonomously in ambiguous territory.</li>
<li>Passionate about self-driving technologies and frontier AI, and about what a small, world-class team can do with the right infrastructure.</li>
</ul>
<div> </div>
<div><span><strong>Bonus/nice to have: </strong></span></div>
<ul>
<li><span>Internal developer platform, research platform, or DevEx work — with a story about a tool whose adoption you grew from zero.</span></li>
<li><span>Large-scale distributed GPU training: hundreds to thousands of accelerators, NCCL, high-performance cluster networking, collective communication tuning.</span></li>
<li><span>High-throughput loading of LiDAR or camera data, and formats such as Parquet or WebDataset.</span></li>
<li><span>Workflow and scheduling systems — Argo Workflows, Ray, Flyte, Kubeflow, or Slurm.</span></li>
<li><span>Build-system depth (Bazel or similar), including remote caching in a monorepo.</span></li>
<li><span>Simulation infrastructure or large-scale batch evaluation pipelines.</span></li>
<li><span>Background in ML, robotics, or autonomous systems infrastructure.</span></li>
<li><span>Security- and IP-sensitive production environments.</span></li>
<li><span>Open-source contributions to ML infrastructure or developer tools.</span></li>
</ul>

Senior / Staff ML Ops Engineer

<div><span>Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.</span></div>
<div> </div>
<div><span>With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: </span><a href="http://www.waabi.ai" rel="noopener noreferrer ugc" target="_blank">www.waabi.ai</a></div><div><br /></div><p><span>The latest advances in AI tooling is fundamentally changing all teams' ways of working - from ML model development, to traditional software development, to non-technical business functions. We are building a dedicated AI Technical Strike Team to establish centralized best practices for working with AI across Waabi with the aim of maximizing productivity. We are looking for hands-on senior software engineers to build the AI tooling, agents, and workflows that accelerate how we work. As a Software Engineer on the AI Tech Strike Team, you will design, build, and ship the tools that compound the productivity of every team at Waabi. The team will own a mix of core tooling work and deployed engagements, where members of the team work closely with functions across Waabi to understand their pain points and co-design AI workflows to maximize productivity.</span></p>
<h2><strong><span>You will...</span></strong></h2>
<ul>
<li>
<p><span>Design, build, and ship custom internal tooling, agents, and workflows tailored to Waabi — supporting our autonomy codebase, simulation environments, ML training pipelines, and research workflows, as well as agents and automations for business teams.</span></p>
</li>
<li>
<p><span>Integrate and extend third-party AI tools — from coding assistants and agents (e.g., Claude Code, Cursor, GitHub Copilot) to AI tools for non-engineering functions — building the integrations, configurations, and internal extensions that make them work well for Waabi.</span></p>
</li>
<li>
<p><span>Evaluate new AI tools, models, and agent frameworks as they emerge, run structured pilots, and provide clear technical recommendations that inform build-vs-buy decisions.</span></p>
</li>
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<p><span>Partner directly with engineers, researchers, and business teams to understand their workflows, prototype solutions quickly, and iterate based on real usage.</span></p>
</li>
<li>
<p><span>Build observability around AI tooling at Waabi — tracking usage, adoption, cost, and quality — so we can prioritize with data and understand impact.</span></p>
</li>
<li>
<p><span>Implement the guardrails for responsible AI tooling at Waabi in partnership with Security, Legal, IT, and Infrastructure.</span></p>
</li>
<li>
<p><span>Drive adoption of the tools you build through documentation, enablement, office hours, and hands-on support — helping every team get more out of their tools every week.</span></p>
</li>
</ul>
<h2><strong><span>Qualifications:</span></strong></h2>
<ul>
<li>
<p><span>5+ years of software engineering experience, with a track record of shipping tools, platforms, or products used by large internal or external user bases.</span></p>
</li>
<li>
<p><span>Deep, hands-on familiarity with the current generation of AI coding tools and agents (Claude Code, Cursor, Copilot, Codex, or similar), and demonstrated experience building and shipping AI-powered products, agents, or workflows — including prompt engineering, evals, tool use, and integrating LLMs into production systems.</span></p>
</li>
<li>
<p><span>Strong proficiency in Python (or another common programming language), and comfort working across modern build systems, CI/CD pipelines, and developer infrastructure.</span></p>
</li>
<li>
<p><span>Strong product instincts and judgment: you can identify high-leverage problems across a diverse set of teams, scope pragmatic solutions, and ship things that actually get adopted.</span></p>
</li>
<li>
<p><span>Strong written and verbal communication skills, comfortable operating with a high degree of autonomy in ambiguous, fast-moving territory and influencing engineers, researchers, and business partners.</span></p>
</li>
<li>
<p><span>Passionate about self-driving technologies, frontier AI, and using AI to push the boundary of what a small, world-class team can accomplish.</span></p>
</li>
</ul>
<h2><strong><span>Bonus/nice to have:</span></strong></h2>
<ul>
<li>
<p><span>Prior experience in a developer productivity, platform engineering, or AI tooling role, ideally with exposure to ML, robotics, or autonomous systems infrastructure (e.g., training pipelines, simulation tooling, large monorepos).</span></p>
</li>
<li>
<p><span>Experience working effectively with non-engineering stakeholders — translating business workflows into technical solutions for functions like Finance, Commercial, Marketing, HR, Legal, and Operations.</span></p>
</li>
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<p><span>Experience building AI workflows or automation for business functions outside of engineering (e.g., finance automation, marketing operations, HR workflows).</span></p>
</li>
<li>
<p><span>Experience deploying and operating AI systems in security- and IP-sensitive environments.</span></p>
</li>
<li>
<p><span>Contributions to open-source AI tooling, agent frameworks, or developer tools.</span></p>
</li>
</ul>

Senior / Staff Software Engineer, AI Tooling

<div><span>Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.</span></div>
<div> </div>
<div><span>With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: </span><a href="http://www.waabi.ai" rel="noopener noreferrer ugc" target="_blank">www.waabi.ai</a></div><div><br /></div><div><span>At the heart of our mission is an unwavering commitment to safety. We are seeking a curious and analytical Safety Methodologies Intern to support the development and implementation of the frameworks that underpin our driverless readiness decisions. This is a unique opportunity to gain hands-on experience in the autonomous vehicle industry, and you will be helping us quantitatively validate the safety of our autonomous trucking solution using our highly realistic simulator and real-world data. We are looking for a driven, inquisitive self-starter who will use their diverse background to dive headfirst into our data and tooling to generate actionable insights for the Safety team.</span></div>
<div> </div>
<div><strong>Application Instructions:</strong></div>
<div><span>- To be considered for an internship/co-op, please add your most up to date academic transcripts alongside your resume for further review.</span></div>
<div><span>- The Summer Internship is expected to start May/June 2027 and continue on a full-time basis (40 hours/week) to August/September 2027.</span></div>
<div> </div>
<div><strong>You will…</strong></div>
<div><span>- Develop Python scripts and tools to calculate quantitative metrics and visualize complex data for collision assessment.</span></div>
<div><span>- Support optimization of data pipelines and automated assessment workflows used for safety validation.</span></div>
<div><span>- Explore large crash databases and apply multivariate modeling techniques to predict collision related metrics. Gain an in-depth understanding of the ins and outs of publicly available safety resources.</span></div>
<div><span>- Conduct A/B testing on collision models using metrics that you develop within the context of our broader safety framework.</span></div>
<div><span>- Collaborate with the safety team to integrate the models you develop into safety evaluation pipelines.</span></div>
<div> </div>
<div><strong>Qualifications:</strong></div>
<div><span>- Currently pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Mathematics, Statistics, or a related field.</span></div>
<div><span>- Experience identifying and handling missing values, anomalies, and outliers in large-scale, real-world datasets.</span></div>
<div><span>- Solid foundation in regression, classification, and hypothesis testing, with the ability to surface patterns in large-scale tabular data.</span></div>
<div><span>- AI-native — uses AI tooling to rapidly experiment, prototype, and harden code, and can rigorously validate the results.</span></div>
<div><span>- Strong Python scripting skills, including hands-on experience with data manipulation libraries (e.g., Pandas, NumPy).</span></div>
<div>
<div><span>- Able to communicate complex technical concepts clearly and accurately.</span></div>
<div>
<div><span>- Collaborative team player who works effectively across functional boundaries.</span></div>
<div><span>- Genuinely excited about self-driving technology and drawn to solving hard, unsolved problems.</span></div>
</div>
</div>
<div> </div>
<div><strong>Bonus/nice to have:</strong></div>
<div><span>- Previous internship or academic experience in robotics, automotive, or data science.</span></div>
<div><span>- Experience with SQL or other data mining tools.</span></div>
<div><span>- Experience creating data visualizations or dashboards.</span></div>

Safety Methodologies Internship/Co-op 2027

<h2><span>You Will…</span></h2>
<ul>
<li>
<p><span>Design and develop data-driven and machine-learned approaches to vehicle control problems, bringing modern ML to a domain traditionally solved with classical methods.</span></p>
</li>
<li>
<p><span>Develop learned models of vehicle behavior and dynamics, and integrate them into the closed-loop simulation.</span></p>
</li>
<li>
<p><span>Apply machine learning to improve how the controller adapts across vehicles and operating conditions.</span></p>
</li>
<li>
<p><span>Be part of a team of multidisciplinary Engineers and Research Scientists using an AI-first approach to enable safe self-driving at scale.</span></p>
</li>
<li>
<p><span>Own problems end to end, from conceptualization and offline experimentation through simulation and on-vehicle validation.</span></p>
</li>
<li>
<p><span>Build the data pipelines, evaluation metrics, and tooling needed to measure whether a learned approach outperforms the classical baseline.</span></p>
</li>
<li>
<p><span>Participate and share ideas in technical and architecture discussions, helping define how learning and classical control coexist in a safety-critical stack.</span></p>
</li>
</ul>
<h2><strong><span>Qualifications:</span></strong></h2>
<ul>
<li>
<p><span>MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Robotics, Controls, Mechanical/Electrical Engineering, Computer Science and/or similar technical field(s) of study.</span></p>
</li>
<li>
<p><span>Demonstrated depth in control theory and dynamic systems (e.g., MPC, optimal control, state estimation, system identification, kinematic and dynamic vehicle modeling).</span></p>
</li>
<li>
<p><span>Hands-on experience applying machine learning to a physical system, with real hardware in the loop rather than simulation alone.</span></p>
</li>
<li>
<p><span>Production-quality coding skill in Python and C++, and experience with deep learning frameworks such as PyTorch.</span></p>
</li>
<li>
<p><span>Solid problem solving skills using linear algebra, optimization, statistics &amp; probability.</span></p>
</li>
<li>
<p><span>Ability to rapidly prototype and test new algorithms, and to design the experiments that prove whether they work.</span></p>
</li>
<li>
<p><span>Open-minded and collaborative team player with the willingness to help others.</span></p>
</li>
<li>
<p><span>Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.</span></p>
</li>
</ul>

Waabi

Open jobs at Waabi

Senior / Staff Research Engineer, Generative AI Deployment

Senior / Staff ML Ops Engineer

Senior / Staff Software Engineer, AI Tooling

Safety Methodologies Internship/Co-op 2027

Senior / Staff Machine Learning Engineer, Vehicle Dynamics

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