
Research Engineer, Scaling
- On-site
- Palo Alto, California, United States
- Artificial Intelligence (AI)
Job description
Target start date: Immediately. Relocation provided.
Since its founding in 2015, 1X has been at the forefront of developing advanced humanoid robots designed for household use. Our mission is to create an abundant supply of labor via safe, intelligent humanoids. At 1X, you'll own critical projects, tackle unsolved research problems, deliver great products to customers, and be rewarded based on merit and achievement.
As a Research Engineer, Scaling, you'll build the systems that let every team and every robot go faster: training more often, evaluating more reliably, and deploying better models to our growing fleet. You'll transform prototypes into production-scale infrastructure for learning and inference, enabling larger training runs and maximizing edge compute utilization to make our models more capable.
Tech Stack
Linux
Python / C++
PyTorch / TorchTitan / TensorRT
Triton / CUDA
Location
The role is based in Palo Alto, CA. Candidates are expected to be in-person at the office.
Responsibilities
High agency and ownership on scaling capabilities in distributed training and/or inference
Ensure that compute is never the bottleneck, i.e. we always have more compute available than data
Enable large-scale (1000+ GPU) training on billion frames+ of robot data, from fault tolerance to distributed ops to experiment management
Optimize high-throughput datacenter scale distributed inference for world models: work on the world's fastest diffusion inference engine
Improve low-latency on-device inference for a variety of robot policies with quantization, scheduling, distillation and more
Job requirements
You must be scaling-pilled, and believe that scale will enable humanoid robots to exist, and be excited about being on the team that will make that happen for the first time in human history
Python and/or C++ programming experience
An intuitive understanding of training or inference scaling and what makes models run fast or slow
Ideal Experiences
Degree in Computer Science or a related field
Hands-on experience with distributed training (TorchTitan/Accelerate/DeepSpeed, FSDP/ZeRO, NCCL), multi-node debugging, and experiment management
Depth in inference performance: TensorRT or similar graph compilers, batching/scheduling, and serving systems
Real familiarity with quantization (PTQ, QAT; calibration strategies; INT8/FP8; libraries such as TensorRT ModelOpt, bitsandbytes, or equivalent)
Experience writing or tuning CUDA/Triton kernels and leveraging vectorization, tensor cores, and memory hierarchy
Sample Projects
We encourage you to apply even if you do not meet every single qualification. Here are some example projects you might work on at 1X. If you have direct experience in solving one of the "sample projects" listed below, please let us know in your cover letter.
Quantizing, pruning, distilling, and optimizing a model to run as fast as possible on a given hardware SKU
Creating or contributing to large-scale, high-throughput inference engines for diffusion models or LLMs, like xDiT, SGLang, vLLM, or TensorRT-LLM
Training large models on hundreds to thousands of GPUs, and designing the infrastructure to scale small experiments to large production runs
Interview Process
The team reviews your CV and statement of exceptional work
15 minute phone conversation with our talent acquisition team
45-minute virtual interview with a team member asking a coding question in the language of your choice.
On-site interview (in-person or virtual) consisting of 4 technical interviews (mix of coding, systems design, open-ended research interview)
Background reference checks
Offer
Compensation
At 1X your work and results will be rewarded with a total rewards package consisting of a base salary, stock options and benefits. Base salary range is $180,000 to $300,000. Your actual salary will be based on your knowledge, skills and experience.
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Our mission is to design Androids that work alongside people, to meet the world’s labor demands and build an abundant society.


