C++ AI Jobs

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This involves understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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The Industrial Security Lead is responsible for defining operational domains and evaluating the reliability of AI capabilities developed in-house. They develop and extend state-of-the-art methods in uncertainty quantification and uncertainty calibration. Their role involves understanding the AI systems built by the company, interfacing with them, and evaluating their robustness in both real-world and adversarial scenarios. They contribute to impactful projects and collaborate with personnel across various teams and backgrounds.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems we build, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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As a Model Optimization & Deployment Engineer, you will optimize large-scale models (LLMs, VLMs) using advanced quantization techniques such as PTQ, QAT, mixed-precision inference workflows, and parameter-efficient fine-tuning methods like LoRA and QLoRA. You will architect and implement model conversion and compilation pipelines using TensorRT and TensorRT-LLM for deployment on edge devices. The role involves performing rigorous parity checking, accuracy recovery, and latency benchmarking between PyTorch frameworks and compiled edge binaries. You are responsible for writing and optimizing custom CUDA kernels and TensorRT Plugins to maximize memory bandwidth and minimize latency on AI accelerators. Furthermore, you will write production-level, highly concurrent, memory-safe C++ and Python code to ensure real-time, deterministic execution of inference on vehicle System on Chips (SOCs).

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As a Senior Modeling & Simulation Engineer, responsibilities include developing models and infrastructure for the integrated simulation pipeline in C++, designing deterministic, high-performance simulation tools capable of faster-than-real-time execution for development, testing, and release, implementing test scenarios and writing unit, system, and regression tests. Collaborate across autonomy, embedded, GNC, and test engineering teams to ensure the simulation mirrors real aircraft behavior and mission scenarios. Contribute to platform-agnostic simulation tooling to accelerate future development efforts. Perform verification and validation (V&V) analysis on model tools. Conduct system performance analysis and generate reports and visualizations. Utilize best practices in C++, simulation architecture, and performance engineering.

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark AI/ML/DL models and publish the models, data sets, and evaluations; deploy models in production by containerizing them; work with customers and internal employees to refine model quality; establish continuous learning pipelines for models using online or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, enabling sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms for high precision manipulation of complex or deformable objects. Collaborate with software engineers to optimize and deploy research prototypes on robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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Design and train state-of-the-art machine learning algorithms for semantic segmentation, object detection, and classification tailored to aerial imagery. Build high-level tactical features on top of base semantic data, such as real-time road vectorization, trafficability analysis, and dynamic obstacle mapping. Architect pipelines that temporally and spatially align semantic data from multiple moving UAVs into a cohesive Common Operational Picture (COP). Optimize and deploy these algorithms directly into the tactical C2 platform, utilizing quantization, pruning, and hardware acceleration to meet strict real-time compute constraints.

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As a Guidance and Controls engineer, you will be responsible for creating and maintaining all control and autonomy algorithms within the XBAT code base. This includes algorithm development, unit tests, component tests, flight software qualification, and flight test support. You will also be responsible for helping update and validate the truth models as required.