Flight Research Senior Machine Learning Engineer at JOBY AERO INC

Santa Cruz, California, United States -

Full Time

Start Date

Immediate

Expiry Date

07 Sep, 26

Salary

245000.0

Posted On

09 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Computer Vision, Deep Learning, C++, Python, PyTorch, Lidar, Radar, 3D Situational Awareness, MLOps, Geometric Vision, Model Training Pipelines, Embedded Systems, CI/CD, Data Curation, System Integration

Industry

Airlines and Aviation

Description

Company Overview Joby Flight Research designs, develops, and flight-tests novel aircraft using a software-first autonomy approach. We build and deploy autonomy, perception, planning, and radar systems across conventional, electric, and hydrogen-electric aircraft in both CTOL and VTOL configurations. Overview Joby Flight Research is seeking a Senior Machine Learning Engineer to build state-of-the-art perception and reasoning algorithms into the Superpilot™ autonomy stack, enabling autonomous aircraft and associated ground systems to safely and autonomously navigate their complex environment, and define the standard of autonomous flight. In this role, you will be responsible for training models that enhance current Superpilot™ algorithms while investigating the application of cutting-edge research to expand autonomous flight capabilities. Your responsibilities will include filtering sensor data from flights, architecting dependable training infrastructure for datasets and models, and performing ongoing evaluations within our Operational Design Domain (ODD). By focusing on these tasks, you will play a vital part in achieving the detection and localization standards necessary for ensuring the safety of autonomous aviation. We are a small, high-impact team that values curiosity, technical initiative, and the ability to operate independently. You will collaborate deeply with controls, and flight software engineers to build a foundation that accelerates our path to safe, autonomous flight. The right candidate is a strong ML engineer who cares deeply about system integration, experiment reproducibility and traceability, and is able to contribute to different parts of the data pipeline where needed. Responsibilities Architect and deploy sophisticated algorithms for aircraft environment detection and tracking. By integrating deep learning with geometric computer vision, you will utilize multi-sensor inputs—including lidar, radar, and varied camera systems—to establish comprehensive 3D situational awareness. Construct high-performance model training pipelines and extensive evaluation systems. These frameworks must ensure reliability by identifying performance nuances in complex edge cases and rare operational scenarios. Work across multi-disciplinary teams—including controls, systems, and flight testing—to seamlessly embed and validate algorithms within our concept of operations. Drive continuous improvement of perception stacks through simulation. You will focus on optimizing latency and robustness to ensure peak performance during demanding flight conditions. Engineer specialized diagnostic and visualization tools to extract meaningful insights from field data. This involves facilitating swift root-cause analysis and resolving perception challenges in active deployments. Shape the future of the Superpilot™ autonomy system. Your contributions will define a perception architecture that sets new benchmarks for safety and reliability in the autonomous aviation industry. Required Proven Professional Background: At least 6 years of experience developing and implementing advanced perception frameworks for autonomous platforms, including aircraft, vehicles, or robotics. Bachelor's degree +8 years experience; Master's degree + 6 years experience; PhD degree + 3 years experience. Deep Learning Specialization: Extensive practical knowledge of cutting-edge models for tracking and detecting objects within real-world applications. Computer Vision Proficiency: Comprehensive understanding of geometric vision methods such as visual odometry, structure-from-motion, and stereo vision to facilitate accurate 3D estimation. Technical Toolset: Competency in C++, Python, and PyTorch, along with various deep learning inference and training ecosystems. Problem-Solving Aptitude: A creative approach to engineering that includes a history of mitigating performance delays and addressing complex operational edge cases. Operational Support: Direct experience maintaining commercial autonomous systems by diagnosing technical hurdles and improving live system dependability. Collaborative Communication: Strong interpersonal skills necessary to succeed within a fast-paced, multidisciplinary team focused on safety-critical engineering. Desired Expertise in developing supporting data curation and model experimentation pipelines to support ML experimentation. Experience deploying models onto GPUs and other accelerators for production-level embedded systems. Experience with autonomous vehicles Experience in processing aircraft data (GPS, inertial, air data, radio data, etc.) Expert-level software engineering: deep expertise in architecting and writing clean, scalable, and maintainable code Experience with version control and CI/CD platforms, able to manage your software through its entire lifecycle (development, testing, deployment) Experience deploying ML models in a production environment using modern MLOps principles and tools (e.g., MLflow, Kubeflow) Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $202,100 - $245,000/yr. The compensation package will be determined by job-related knowledge, skills, and experience. Joby also offers a comprehensive benefits package, including paid time off, healthcare benefits, a 401(k) plan with a company match, an employee stock purchase plan (ESPP), short-term and long-term disability coverage, life insurance, and more. Additional Information Joby Aviation is an equal opportunity employer.

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Responsibilities

Architect and deploy sophisticated perception and reasoning algorithms for aircraft environment detection and tracking. Build high-performance model training pipelines and evaluation systems to ensure safety and reliability in autonomous flight.