Who We Are

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale—empowering a safer, richer, and more connected future.

About the Role:

Nuro is seeking an experienced Technical Lead Manager with deep expertise in quantized training and model compression to join our ML Infrastructure team. In this role, you will drive the adoption of state-of-the-art quantization techniques, enabling training and deployment of highly-efficient models that power the Nuro Driver™. You will lead technical strategy, mentor a team of engineers and researchers, and partner closely with research and product groups to ensure our ML infrastructure is optimized for both cutting-edge research and real-time deployment on autonomous vehicles.

About the Work:

As TLM for Nuro's ML Training Infrastructure you will improve model training efficiency and drive the adoption of state-of-the-art quantization techniques. This will include:

• Setting technical direction for the Training Infrastructure team.

• Staying ahead of emerging research and evaluating new methods.

• Establishing telemetry to root-cause quality regressions in lower precision training.

• Driving the adoption of quantized training methods (e.g., AWQ, AQT, GPTQ) across Nuro’s ML infrastructure to accelerate model training and inference.

• Leading the design and implementation of efficiency initiatives for model training, including low-bit quantization, pruning, and knowledge distillation, for both research and production workloads.

• Collaborating cross-functionally with research, infrastructure, and product teams balancing accuracy, latency, and resource constraints.

• Mentoring and growing a high-performing team of engineers and researchers.

About You

• 6+ years of professional or research experience in ML infrastructure, distributed training, or ML systems engineering.

• Hands-on experience with quantization methods, including Activation-Aware Weight Quantization (AWQ), Accurate Quantized Training (AQT), FP-8 training, or related methods.

• Knowledge of broader model compression techniques, such as structured/unstructured pruning and knowledge distillation.

• Experience building or maintaining quantization libraries(e.g., AQT, bitsandbytes, NVIDIA Transformer Engine, Deep Speed Compression).

• Understanding of calibration and scaling strategies for quantized models to minimize accuracy loss.

Bonus Points

• Advanced degree (Ph.D. or strong M.Sc. with research experience) in Computer Science, Electrical Engineering, or related fields.

• Knowledge of sparse networks and complementary model compression techniques (e.g., Ada Round, BRECQ, structured pruning).

• Published work or open-source contributions in quantization methods (e.g., AWQ, AQT, GPTQ, Smooth Quant, Zero Quant).

At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $235,030 and $352,290 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.

At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #