Top AI & Machine Learning Jobs

Develops and transfers AI research into real-world autonomous aircraft and vehicle systems. Evaluates multimodal perception models, builds prototypes, combines learned and classical computer vision methods, and optimizes capabilities for edge deployment. The role involves Python and C++ software, sensor fusion, model evaluation, robotics, and collaboration with researchers and autonomy engineers to deliver tested, documented production capabilities.

Develop deep learning and 3D computer vision models for state estimation, visual navigation, localization, depth, correspondence, and pose estimation. Own data preparation, training, evaluation, benchmarking, and reproducible workflows. Integrate learned measurements into VIO and terrain-relative navigation systems, optimize models for onboard compute, and collaborate with deployment, software, systems, and flight-test teams to deliver production-ready Hivemind SDK components.

Own production operations for workplace AI platforms, including configuration, integrations, observability, secrets, access controls, model and prompt changes, benchmarking, and incident response. Translate technical findings into governance, risk, compliance, and training materials while collaborating with Security, Legal, HR, and business stakeholders. Investigate failures, implement mitigations, document root causes, and maintain reliable, secure AI services.

Build production AI systems that improve technical learning for Hivemind engineers and customers. Develop an AI training coach, LLM-powered tooling, workshops, curricula, labs, assessments, and content automation. Integrate generative AI, retrieval, agents, and evaluation capabilities while maintaining strong software engineering, testing, observability, security, and operational standards. Collaborate with autonomy experts, engineers, applications engineering, and customer success to accelerate technical proficiency and product adoption.

Design, research, and deploy state-of-the-art deep learning perception capabilities for autonomous and non-autonomous platforms. Develop computer vision models, deploy AI systems to edge devices, and build tools that scale deep learning operations. Contribute to R&D, model optimization, embedded production deployments, MLOps systems, and technical mentoring.

Research, design, implement, and deploy state-of-the-art deep learning perception solutions for autonomous and non-autonomous platforms. Develop computer vision capabilities, deploy AI systems to edge devices, build scalable deep learning tools, contribute to R&D, and collaborate with engineers. The role also involves applying modern architectures, optimizing embedded production models, supporting MLOps, and potentially mentoring junior team members.

Lead the architecture and implementation of a Kubernetes-native AI Factory platform for distributed training, simulation, evaluation, inference, and deployment. Build GPU infrastructure, scheduling, storage, networking, observability, model lifecycle capabilities, and self-service developer workflows across cloud, on-premises, sovereign, and air-gapped environments. Partner with ML researchers and autonomy teams while providing technical leadership on scalable, maintainable AI infrastructure.

Design, train, fine-tune, evaluate, and deploy state-of-the-art vision, vision-language, and vision-language-action models for autonomous systems. Build scalable data pipelines, SFT workflows, evaluation frameworks, and optimize models for embedded hardware using ONNX and TensorRT. Collaborate with cross-functional teams to translate research into robust, production-ready perception capabilities for defense platforms.

Lead design, training, fine-tuning, evaluation, and deployment of vision, vision-language, and vision-language-action models for autonomous systems. Build scalable data pipelines, SFT workflows, and evaluation frameworks. Optimize models for embedded hardware (ONNX, TensorRT), collaborate cross-functionally, translate research to production, and mentor engineers.

Design, train, fine-tune, evaluate, and deploy vision, vision-language, and vision-language-action models for autonomous systems. Build scalable data pipelines, SFT workflows, evaluation frameworks, and optimize models for embedded hardware (ONNX, TensorRT). Collaborate with researchers and engineers to translate ML research into robust, production-ready perception capabilities for defense platforms.