Photo courtesy of Tesla, Inc.
Artificial intelligence (AI) will undoubtedly play a key role in the further development of mankind and Tesla is actively developing in this direction. By creating hardware and software for use in vehicles, robots, and much more, the company needs a constant influx of highly qualified specialists. As such, it is currently expanding its recruitment to a number of positions on its AI team.
Tesla believes that an approach based on advanced artificial intelligence for vision and planning, supported by the efficient use of inference equipment, is the only way to achieve an overall solution for full self-driving and beyond. The company has several divisions focused on specific developments: FSD Chip, Dojo Chip, Dojo Systems, Neural Networks, Autonomy Algorithms, Code Foundations, Evaluation Infrastructure, and Tesla Bot. Tesla CEO Elon Musk extended an invitation to apply for employment: “hardcore AI engineers who care about solving problems that directly affect people's lives in a major way.”
As always, Tesla is looking for hardcore AI engineers who care about solving problems that directly affect people’s lives in a major way.https://t.co/0B5toOOHcj— Elon Musk (@elonmusk) December 6, 2021
Build AI inference chips to run Full Self-Driving software, considering every small architectural and micro-architectural improvement while squeezing maximum silicon performance-per-watt. Perform floor-planning, timing and power analyzes on the design. Write robust tests and scoreboards to verify functionality and performance. Implement drivers to program and communicate with the chip, focusing on performance optimization and redundancy. Finally, validate the silicon chip and bring it to mass production in our vehicles.
Build AI training chips to power Tesla's Dojo system. Implement bleeding-edge technology from the smallest training nodes to the multi-die training tiles. Design and architect for maximum performance, throughput and bandwidth at every granularity. Dictate physical methodology, floor-planning and other physical aspects of the chip. Develop pre-silicon verification and post-silicon validation methods to ensure functional correctness. Write compilers and drivers to optimize power and performance for the company's neural networks throughout the entire Dojo system.
Design and build the Dojo system, from the silicon firmware interfaces to the high-level software APIs meant to control it. Solve hard problems with state-of-the-art technology for high-power delivery and cooling, and write control loops and monitoring software that scales. Work with every aspect of system design where the limit is only your imagination, employing the full prowess of Tesla's mechanical, thermal and electrical engineering teams to create the next-generation of machine learning compute for use in the company's data centers. Collaborate with Tesla fleet learning to deploy training workloads using our massive datasets, and design a public facing API that will bring Dojo to the masses.
Apply cutting-edge research to train deep neural networks on problems ranging from perception to control. Tesla's per-camera networks analyze raw images to perform semantic segmentation, object detection and monocular depth estimation. The company's birds-eye-view networks take video from all cameras to output the road layout, static infrastructure and 3D objects directly in the top-down view. Networks learn from the most complicated and diverse scenarios in the world, iteratively sourced from Tesla's fleet of more than 1M vehicles in real-time. A full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hours to train. Together, they output 1,000 distinct tensors (predictions) at each timestep.
Develop the core algorithms that drive the car by creating a high-fidelity representation of the world and planning trajectories in that space. In order to train the neural networks to predict such representations, algorithmically create accurate and large-scale ground truth data by combining information from the car's sensors across space and time. Use state-of-the-art techniques to build a robust planning and decision-making system that operates in complicated real-world situations under uncertainty. Evaluate your algorithms at the scale of the entire Tesla fleet.
Throughput, latency, correctness and determinism are the main metrics Tesla optimize its code for. Build the Autopilot software foundations up from the lowest levels of the stack, tightly integrating with the company's custom hardware. Implement super-reliable bootloaders with support for over-the-air updates and bring up customized Linux kernels. Write fast, memory-efficient low-level code to capture high-frequency, high-volume data from Tesla's sensors, and to share it with multiple consumer processes— without impacting central memory access latency or starving critical functional code from CPU cycles. Squeeze and pipeline compute across a variety of hardware processing units, distributed across multiple system-on-chips.
Build open- and closed-loop, hardware-in-the-loop evaluation tools and infrastructure at scale, to accelerate the pace of innovation, track performance improvements and prevent regressions. Leverage anonymized characteristic clips from our fleet and integrate them into large suites of test cases. Write code simulating our real-world environment, producing highly realistic graphics and other sensor data that feed our Autopilot software for live debugging or automated testing.
Develop the next generation of automation, including a general purpose, bi-pedal, humanoid robot capable of performing tasks that are unsafe, repetitive or boring. Tesla is seeking mechanical, electrical, controls and software engineers to help it leverage its AI expertise beyond its vehicle fleet.
© 2021, Eva Fox | Tesmanian. All rights reserved.
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