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Develops machine learning models for autonomous vehicle perception, including sensor fusion and spatial-temporal representation learning, using large-scale real-world driving data.
Principal Software Engineer at Waymo builds and optimizes sensor-fusion foundation models for autonomous driving, using large-scale multi-modal data and deep learning toolkits.
Designs and optimizes large-scale machine learning pipelines for vision-language models to improve autonomous vehicle perception, using Python and deep learning frameworks.
Builds AI-powered developer tools and multi-agent systems to accelerate autonomous-driving software development, focusing on generative AI and spec-driven coding.
Lead the design and development of machine learning systems to evaluate and improve Waymo's autonomous driving technology, focusing on reinforcement learning, generative models, and large-scale simulation workflows.
Develops AI-driven automation for autonomous vehicle sensor maintenance and fleet operations, using machine learning and robotics to improve efficiency in a hybrid work environment.
Leads the design and implementation of evaluation systems for large vision and language models used in autonomous driving, building benchmarks to assess model quality, safety, and realism.
Senior ML Engineer builds and deploys computer vision and vision-language models to generate high-fidelity labels for autonomous driving, using deep learning, generative AI, and reinforcement learning at scale.
Build and scale large vision-language foundation models for autonomous driving, using multimodal pre-training and reinforcement learning to improve scene understanding and autolabeling.
Applied AI Engineer This is a remote, client-facing consultant role with Inviso. In this position, you will support an external software development organization as part of Inviso’s delivery team, helping build…
Designs, builds, and ships C++/Python autonomy software — perception, localization, sensor fusion, navigation, and planning — for Mach Industries' autonomous defense platforms, taking systems from prototype through simulation, HITL, and flight test to deployment on embedded compute in GPS-denied environments.
Machine Learning Engineer at a defense-tech company who owns the training, data, and edge-inference backbone for vision and multi-sensor autonomy models — building data/training pipelines, generating synthetic data, and deploying PyTorch models in real time on Jetson-class embedded hardware.
AI/ML engineer at DataZymes in Bengaluru building and productionizing GenAI applications (RAG, multi-agent systems, Text2SQL, fine-tuning) for healthcare/pharma analytics clients. Core stack: HuggingFace, LangChain, DSPy, pandas, scikit-learn, PyTorch, and cloud ML platforms (AWS/Azure/GCP); Databricks/Spark is a plus.
Leads product strategy and roadmap for model training and fine-tuning in SeekrFlow, Seekr's end-to-end AI platform: turning ML workflows like SFT, LoRA, DPO/RLHF, dataset management, evaluation, and model promotion into enterprise-ready platform capabilities. Hybrid role based in offices in Austin, TX or Reston, VA.
Seekr is hiring an AI Engineer to develop and deploy generative and predictive AI solutions for US Government customers. The role involves building agentic workflows, fine-tuning LLMs, and creating prototype applications using Python and various AI frameworks.
Write and maintain docs for Seekr’s AI platform and developer tools, covering installation, deployment, APIs, and AI concepts for technical users.
Design and operate Kubernetes-based GPU infrastructure and AI serving platforms for large-scale model training, inference, and autonomous agents using Python, Go, or Rust.
Forward Deployed Engineers embed with clients to deploy production-grade AI systems using SeekrFlow, specializing in LLM fine-tuning, agentic workflows, and Kubernetes-based deployments across cloud/on-prem environments.
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