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. 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: www.waabi.ai As a Research Engineer in Neural Rendering, you will create the next generation of multi-sensor rendering systems for autonomous driving. You will collaborate with our team of world-renowned scientists and engineers to build innovative, practical, and scalable rendering and content creation solutions for self-driving. We value original, high-impact ideas and rigorous experimental validation. You will… - Be part of a team of multidisciplinary Research Scientists and Engineers working on building a best-in-class multi-sensor simulation stack. - Use cutting edge techniques in reconstruction, neural rendering and generative AI to build large-scale, efficient digital twins from real-world camera, LiDAR, and RADAR data. You will work on shipping next-generation simulation software which mixes traditional real-time rendering with NeRF, 3D Gaussian Splatting, diffusion  models, etc. - Collaborate with Waabi’s autonomy and safety teams to improve the realism and diversity of Waabi World. - Have the opportunity to make contributions to high-impact research papers submitted to top conferences or journals (CVPR, ECCV, ICCV, SIGGRAPH, NeurIPS, ICLR, ICRA, RA-L). Qualifications: - The US yearly salary range for this role is: $134,000 - $235,000 USD - The Canada salary range for this role is: $171,000 - $270,000 CAD In addition to competitive perks & benefits, Waabi US Inc. and Waabi Canada Inc.'s yearly salary ranges are determined based on several factors in accordance with the Company’s compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US and Canada locations.  Note: The Company provides additional compensation for employees in this role, including discretionary equity incentive awards and discretionary annual performance bonus. Perks/Benefits: - Competitive compensation and equity awards. - Health and Wellness benefits encompassing Medical, Dental and Vision coverage (for full-time employees only). - Unlimited Vacation. - Flexible hours and Work from Home support. - Daily drinks, snacks and catered meals (when in office). - Regularly scheduled team building activities and social events both on-site, off-site & virtually. - As we grow, this list continues to evolve! Waabi is a technology start-up building technologies to transform the way the world moves. Join our talented team to be a part of the future and to make an impact! Waabi is an equal opportunity employer. We celebrate diversity and are committed to creating a supportive, inclusive, and accessible workplace for all our employees. We seek applicants of all backgrounds and identities, across race, color, ethnicity, national origin or ancestry, age, citizenship, religion, sex, sexual orientation, gender identity or expression, military or veteran status, marital status, pregnancy or parental status, caregiver status, disability, or any other characteristic protected by law. We make workplace accommodations for qualified individuals with disabilities as required by applicable law. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.

• Building experiments, debugging models, scaling training pipelines, and turning research ideas into working systems. • Work closely with scientists and other engineers to implement new methods, run large-scale experiments, and help shape the infrastructure that supports research programs.

• Define the 'Reddit Benchmark' evaluation standard: Own the methodology — not just the harness — for rigorously measuring model quality across Safety, Reasoning, representation/retrieval, and Reddit-specific knowledge. Decide what 'Reddit-native' means in measurable terms and set the bar the org trains against. • Own evaluation reliability and statistical rigor: Establish the science behind trustworthy evals — judge variance, multi-sample scoring, inter-rater/inter-sample agreement, sampling and temperature effects, and calibration of automated judges. You are accountable for whether a benchmark delta is real or noise. Drive the practice of evaluation as a release gate — offline against frozen datasets, and pre-merge in CI/CD — so regressions are caught before endpoints ship. • Design model-as-a-judge methodology: Own judge selection, prompt design, calibration, and reliability for automated evaluation using frontier external models, enabling rapid, trustworthy iteration cycles. • Set post-training recipes and strategy: Design SFT recipes (data mixtures, curriculum, ablation strategy) that convert base models into helpful, well-aligned endpoints; partner with engineering to scale them. • Evaluate base and CPT checkpoints, not just endpoints: Design checkpoint-selection methodology across CPT experiments and LR studies, so we pick the right base before committing post-training compute. • Drive synthetic data generation strategy: Define and curate high-quality instruction and evaluation sets to improve generalization where human data is scarce. • Partner with Safety Engineering: Translate high-level safety policy into concrete classification metrics, probe sets, and CI/CD unit tests — including precision/recall at threshold, label-noise handling, and false-positive taxonomy for abuse detection (HHV). • Diagnose post-training instability: Dive into loss curves and eval logs to identify alignment tax and capability degradation, and recommend the fix. • Lead research direction: Set technical direction for evaluation and post-training across the team, mentor engineers and scientists, and represent the work internally (and externally where appropriate).

• Design, train, and ship production-grade ML models, including deep learning, NLP, and computer vision systems—that solve complex business problems and power core product features. • Conduct deep exploratory research on massive datasets to uncover novel patterns in user behavior and content creation, translating raw data insights into new predictive modeling opportunities. • Apply advanced fine-tuning strategies (e.g., PEFT, LoRA) to adapt state-of-the-art foundation models to specific domain tasks, rigorously experimenting to maximize performance. • Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation, ensuring high data quality and system reliability. • Optimize model performance for latency, throughput, and resource utilization, balancing model complexity with production constraints (e.g., overfitting vs. underfitting, compute efficiency). • Collaborate cross-functionally with data engineers, product managers, and software engineers to translate business requirements into technical ML specifications and integrate models into user-facing applications. • Champion MLOps excellence by automating deployment workflows, implementing CI/CD for ML, and establishing robust monitoring for model drift and health. • Stay at the forefront of ML research, evaluating novel algorithms and techniques (e.g., Transformer architectures, quantization) to drive innovation and technical strategy.