About Black Sesame Technologies
Founded in July 2016, Black Sesame Technologies is an AI digital imaging technology firm that creates solutions for real-world AI challenges. The company has developed a radically new chip and system to dramatically accelerate deep learning applications for autonomous.
We are innovating at every level of the stack – from chip, to new algorithms and network architectures at the cutting edge of ML research. Our software stack is co-designed with the hardware and developed with scalability and quality in mind. Join us to create revolutionary Chips from the ground up.
Responsibilities
- Participate in the R&D and validation of end-to-end learning-based decision-making and motion planning models for autonomous driving, contributing to the development of next-generation advanced autonomous driving systems.
- Take charge of the design and training of end-to-end Planner models based on foundational architectures such as Transformer. Deeply explore and deploy generative paradigms—such as Autoregressive and Diffusion models—for trajectory generation and behavior prediction.
- Drive the application of Reinforcement Learning (RL) and Imitation Learning (IL) in autonomous driving decision-making and planning. Leverage cutting-edge algorithms (e.g., GRPO) to continuously enhance the model's reasoning and decision-making capabilities in complex, interactive, and game-theoretic scenarios.
- Build and refine the closed-loop simulation evaluation system. Promote large-scale testing, validation, and data closed-loop iteration of end-to-end models within simulation environments.
- Participate in the optimization and on-vehicle deployment of end-to-end models for mass-production projects. Drive the efficient utilization of high-quality data and successful engineering implementation.
- Collaborate with perception and prediction modules to optimize multi-modal feature fusion and model inference mechanisms, enhancing the stability and overall performance of the end-to-end decision-making chain.
Qualifications
- Master's degree or above in Computer Science, Automation, Robotics, Artificial Intelligence, or related fields. Possess a solid theoretical foundation in deep learning and strong mathematical modeling skills.
- In-depth, practical R&D and deployment experience in at least two of the following domains:
- End-to-End Decision & Planning Models: Familiar with foundational network architectures such as Transformer. Possess a deep understanding and practical experience in applying generative models (e.g., Autoregressive, Diffusion) to sequential decision-making.
- Reinforcement Learning & Imitation Learning: Solid theoretical foundation and hands-on system tuning experience in RL or IL.
- Closed-Loop Simulation & System Integration: Familiar with the overall workflow of end-to-end autonomous driving systems. Experience in building closed-loop simulation platforms and joint debugging across perception, planning, and control modules.
- Robust engineering implementation capabilities. Proficient in C++ and Python programming, with mastery of mainstream deep learning frameworks such as PyTorch.
- Familiar with ROS or common autonomous driving middleware. Deep understanding of core model evaluation metrics (e.g., closed-loop success rate, takeover/intervention rate). Candidates with a background in mass-production projects are highly preferred.
- Excellent engineering collaboration and systems thinking abilities. Capable of independently driving the R&D of cutting-edge models within a tightly coupled, multi-module environment.
Pay: $140,000.00 - $400,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Employee assistance program
- Employee discount
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Referral program
- Retirement plan
- Vision insurance
Work Location: In person
