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Develops AI/ML models and data science solutions for national security, space, and civil/commercial applications, focusing on scalable, mission-critical systems.
Senior Data Scientist developing AI/ML, optimization, and control algorithms for Honeywell's building automation and energy management platforms using Python, MATLAB, and Spark, deployed as microservices to cloud and edge environments.
Design and implement scalable, low-latency ML systems and services on Azure Cloud using Python, collaborating across engineering, data science, and product teams to operationalize LLMs, NLP, speech, and forecasting models.
Tenure-track Assistant/Associate Professor conducting AI/ML research applied to veterinary medicine, disease prevention, and population health, while teaching and mentoring veterinary and graduate students at Ohio State University.
Builds and scales distributed training infrastructure for large AI models, optimizing performance and reliability across thousands of GPUs using systems like Megatron-LM and SGLang.
Develop production-ready grasping and manipulation algorithms for industrial robots, building a software layer that abstracts robotics complexity into intuitive workflows using C++ and Python.
Staff Robotics Software Engineer at Intrinsic (Google's AI robotics group) developing and deploying production-ready grasping and manipulation algorithms from research to global deployment, primarily using C++, Python, and applied machine learning.
Research and develop post-training methods (RLHF, SFT, PEFT, reward-based optimization) for Enchant, Iambic's large multimodal transformer model used in drug discovery, using Python and PyTorch at scale.
Research and develop post-training methods for a large multimodal transformer model (Enchant) in drug discovery, focusing on fine-tuning, reinforcement learning, and evaluation frameworks to advance AI-driven therapeutic development.
Builds and integrates autonomy algorithms and software for uncrewed systems, mission planning, and multi-agent collaboration using C++/Java and AI/ML techniques.
Lead a team developing autonomy algorithms and software for uncrewed systems, focusing on mission planning, control, and multi-agent collaboration using C++/Java and advanced analytics.
Perplexity is hiring an AI Software Engineer to build and scale agentic systems that allow users to perform complex tasks across digital environments. The role involves working with frontier AI models, training decision models, and developing browser-based automation tools.
PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast…
Perplexity is seeking a TLM (Tech Lead Manager) to lead and grow our highly driven Agents engineering team. The Agents team consists of AI/ML, backend, and full-stack engineers who collaborate to build delightful…
Part-time AI/ML engineer builds, trains, and deploys models using Python and frameworks like TensorFlow/PyTorch to solve government data challenges.
Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it. OUR MISSION True Anomaly delivers decisive capabilities for space superiority. We build…
About the Role This is a founding GTM hire at an early-stage AI infrastructure company building tools for reinforcement learning environments and AI evaluation. You'll sit at the intersection of engineering and…
This role leads a team in the AIML Evaluation group to build systems that measure and improve foundation models and agentic experiences. The manager will oversee the development of automated evaluation pipelines, synthetic data generation, and model refinement loops to enhance Apple intelligence products.
Principal Architect at 6sense designs and evolves large-scale, distributed cloud systems, setting technical strategy and guiding engineering teams in building scalable, resilient platforms.
This role involves creating reinforcement learning environments and deterministic verification systems to evaluate AI models on complex software engineering tasks using Model Context Protocol (MCP) tools. Engineers will design reproducible scenarios for bug fixing, feature implementation, and refactoring.
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