Job Description

We are seeking a highly skilled and experienced Senior Software Engineer to join our innovative AI robotics company, taking our AI infrastructure to the next level. In this role, you will build robust offline workflows including training, validation, and production of the robot. You will work closely with AI Robotics expertise to build the foundations for next-gen robots.

Why its interesting:

RoboForce, an AI-Robotics company, is building a first-of-its-kind Robotic Workforce System to take on the most tedious, force-demanding, and dangerous work humans don't have to do.

At RoboForce, we're building a small team with a flat org structure and extreme talent density. Currently, we have top tech leaders from CMU Robotics, Michigan Robotics, Amazon Robotics, Tesla Robotics, Google, Waymo, Apple, and Microsoft. RoboForce is backed by world-class investors, including the Nobel Prize Laureate, Carnegie Mellon University, and beyond.

Responsibilities:

1. Design and develop Python-based training infrastructure to ensure stable and accelerated model training.
2. Design and implement software tools for data collection and management, training deep neural networks, and deploying them on RoboForce robots.
3. Develop and optimize tools for deploying trained neural networks to hardware platforms.
4. Design and maintain pipelines for running and validating PyTorch models.
5. Develop, operate, and maintain reliable and scalable backend distributed systems.

Minimum Qualifications:

1. Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field with at least 8 years of working experience.
2. Strong proficiency in both C++ and Python.
3. Decent experience with at least 2 low-level programming languages such as C, C++, or Rust.
4. Experience with Python and an ML framework, including PyTorch, JAX, TensorFlow, etc.
5. Experience using and managing data stores, including Postgres, MySQL, ElasticSearch, Redis, etc.
6. Experience in managing cloud infrastructure, including platforms such as AWS, Azure, and GCP.

Preferred Qualifications:

1. Experience in profiling and optimizing CPU-GPU interactions, including techniques like pipelining compute and data transfers.
2. Experience in scaling neural network training jobs across GPU clusters.
3. Experience with GPU programming with CUDA.
4. Experience in developing data annotation and dataset management tools.

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