Designs evaluation measures, harnesses, and datasets to assess risks from frontier AI systems, including dangerous capabilities testing. Collaborates with agencies, publishes methodologies for policymakers; requires 3+ years ML experience and publications in generative AI.
Conducts research in applied category theory to formalize semantics of probabilistic DSLs and develop models for real-world phenomena like relationships, dynamical systems, and natural language. Requires PhD-level expertise in category theory and functional programming skills.
Designs methods, systems, and experiments for AI controls and monitoring to ensure alignment in high-stakes environments, including real-time tracking, fail-safes, and red-team simulations. Requires 3+ years ML experience, published research in generative AI, and strong prototyping skills.
Research Scientist focuses on agent robustness, developing tests, exploits, and mitigations for safe AI agents. Requires 3+ years ML experience, RL techniques like RLHF/DPO, and published research in generative AI.
Conducts research on post-training methodologies and performant inference for AI models, balancing pure research with applied work for production systems. Requires PhD in ML with top publications and ability to design rigorous experiments at scale.
Conducts research on efficient AI systems focusing on real-time adaptation, model efficiency, and cross-stack optimization. Requires PhD or equivalent, 4-5+ years industry experience, and deep ML expertise including PyTorch/JAX and optimization techniques.
Conduct research and build foundational ML models for structured and tabular data, combining statistical learning theory, probabilistic modeling, and large-scale systems. Requires a PhD and strong experience in tabular/relational ML.
Deploys high-performance vision and multimodal AI models to robotic platforms, optimizing for low-latency inference under hardware constraints. Requires 3+ years in real-time robotics systems, expertise in PyTorch, C++/Rust, and edge deployment.
Develops RLHF and post-training methods for personalized, multimodal AI systems on consumer devices, focusing on reward modeling, preference learning, long-horizon evaluation, and alignment with user values. Requires strong ML research background in RLHF and related areas.
Builds macroeconomic models and scenario-based forecasting tools for transformative AI impacts on growth, labor markets, and income distribution. Requires PhD in Economics, expertise in macro modeling, computational methods, and grounding in real-world AI usage data.
Part-time AI Trainer annotates mortgage conversation data, provides feedback on AI responses, and tests system quality for mortgage servicing calls. Requires 5+ years mortgage customer service experience and industry knowledge.
Designs and implements mitigation stacks to prevent loss of control risks in frontier AI models, including prevention, monitoring, detection, and enforcement. Requires expertise in deep learning, transformers, PyTorch/TensorFlow, and AI safety research.
Senior Applied Researcher develops AI/ML solutions for healthcare challenges, applying GenAI, LLMs, and techniques like RAG to build production-ready models in SaaS environments. Requires Master's in relevant field, healthcare data experience, Python proficiency, and ML frameworks expertise.
Leads strategy and execution for measuring AI model capabilities across training and deployment, developing novel long-horizon evaluations and leading a team of researchers. Requires experience in LLM evaluations, technical leadership, and cross-team collaboration in fast-paced AI research environments.
Conducts advanced research in AI, machine learning, or related fields, developing novel algorithms and publishing results. Requires PhD and strong publication record.
Conducts core research on multimodal foundation models for world-simulations, bridging modeling, data, systems, and evaluation. Requires advanced degree in CS/ML, first-principles scaling intuition, and experience with large-scale GPU training.
Develops bi-directional/recurrent architectures for hierarchical sensorimotor perception inspired by brain principles to advance AGI. Requires PhD-level expertise in deep learning frameworks like PyTorch/JAX, theoretical rigor, and passion for biological intelligence.
Conducts fundamental research on hierarchical RL agents, developing architectures for world modeling, planning, and ethologically constrained learning. Collaborates cross-functionally to translate brain-inspired principles into scalable AGI systems; requires PhD-level expertise in AI/ML and strong coding skills.
Conducts fundamental research on brain-inspired world models and AGI architectures, designing experiments on cognition, causal representations, and unsupervised learning. Collaborates with neuroscientists and engineers; requires PhD-level expertise in deep learning frameworks like PyTorch/JAX.
Conducts fundamental research on neuroscience-inspired energy-based models and hierarchical latent variable models for AGI. Designs experiments on cognition and collaborates cross-functionally to build scalable systems. Requires PhD-level expertise in deep learning and PyTorch/JAX.
Develops AI/ML models to transform sensor data into personalized health and sleep insights, focusing on thermoregulation, foundation models, and behavioral simulations using vast sleep datasets. Requires PhD in ML/AI-related field, 3+ years practical ML experience, and strong publications.
Leads R&D on small language models and AI training, developing efficient architectures, optimizing performance, and ensuring safety. Collaborates with research, engineering, and product teams using Python, PyTorch, TensorFlow, or JAX.
Build state-of-the-art ML models to predict traffic behavior for autonomous driving, using generative sequence modeling and controllable agents for planning and simulation. Requires PhD preferred, 2+ years deploying ML systems, and expertise in PyTorch and robotics ML.
Develops efficient inference engines and RL/post-training pipelines for production-scale LLMs, optimizing algorithms, systems, and performance across the stack. Requires 3+ years in ML systems/RL/inference and advanced degree.
Collaborate on high-impact AI research projects, design and review deep learning models in PyTorch, analyze GPU performance, and co-author publications. Requires PhD in CS/AI/ML and hands-on expertise with Transformers, CNNs, and diffusion models.
University students contribute to generative AI projects like model training and failure mode identification, participate in focus groups, and collaborate on team challenges. Requires STEM enrollment, coding proficiency, and competitive programming experience; fully remote with flexible hours.
Conducts cutting-edge research on data for state-of-the-art AI agents like browser and SWE agents, develops prototypes using LLMs and frameworks like PyTorch/JAX, and publishes in top ML venues. Requires 3+ years ML experience and strong cross-functional communication.
Develops next-gen Agent RL training platform for enterprise GenAI, integrating cutting-edge research to train state-of-the-art models for complex use cases. Requires 5+ years LLM production experience, RLHF expertise, recent top publications, and advanced CS degree.
Develops and deploys state-of-the-art ML models and agents for enterprise GenAI using RL training and post-training algorithms. Requires 1-3 years LLM production experience, RLHF expertise, recent top publications, and advanced CS degree.
Develops and optimizes post-training algorithms for agent RL platforms, focusing on LLM training, inference frameworks, and multi-agent systems. Requires 1-3 years production LLM experience, expertise in PyTorch/CUDA, RLHF/PPO, and advanced degree.
Develops next-generation large language models through research, experimentation, and engineering on pre-training team. Requires strong Python/PyTorch skills, ML expertise, and MS/PhD in related field.
Research Engineer optimizes and scales production pretraining of frontier AI models, handling performance, debugging, experiments, and on-call incidents. Requires expertise in JAX, TPU, PyTorch, or large-scale ML systems with a 50/50 research-engineering balance.
Research Engineer implements and scales post-training techniques like Constitutional AI and RLHF for production AI models, optimizing capabilities, alignment, and safety. Requires strong Python skills, ML systems experience, and ability to handle complex distributed training pipelines.
Builds large-scale infrastructure for AI scientist training, evaluation, and deployment, resolving bottlenecks in distributed systems for scientific AGI. Requires 6+ years in infrastructure engineering with expertise in ML stacks, containers, and data pipelines.
Conducts experimental ML research on AI alignment and safety for powerful systems, focusing on scalable oversight, control, and stress-testing. Requires strong software/ML engineering, empirical research experience, and Python proficiency.
Builds next-generation training environments and evaluations for agentic AI models, blending research in reinforcement learning with robust engineering implementation. Requires strong technical judgment, agency, and experience in ML systems.
Conducts mechanistic interpretability research to reverse-engineer language models, developing methods to understand neural network algorithms for AI safety. Requires scientific research background, Python proficiency, and collaborative engineering mindset.
Research Engineer builds and optimizes reinforcement learning infrastructure for advancing AI capabilities like agentic models, tool use, and reasoning. Requires Python proficiency, ML frameworks experience, and ability to blend research with scalable engineering.
Research Engineer works end-to-end to remove bottlenecks toward scientific AGI, focusing on long-horizon reasoning, computer use, and model capabilities. Requires 8+ years ML experience, expertise in language model pipelines, distributed systems, and collaborative problem-solving.
Research Engineer focused on mechanistic interpretability, building tools and infrastructure to reverse-engineer neural networks for safer AI. Requires 5+ years software experience, Python proficiency, and AI research contributions.
Develops theories of intelligence grounded in neural network internal structures, focusing on belief geometries in LLMs and biological brains. Conducts experiments bridging mathematics, ML interpretability, and safety research; requires PhD-level quantitative depth and hands-on coding.
Leads research in database systems, focusing on query optimization, storage engines, indexing, and hardware-accelerated architectures. Collaborates with engineering to productize innovations and builds a research team. Requires PhD and 12+ years experience.
Develops scalable ML-based planning and prediction systems for autonomous driving trajectories. Requires expertise in sequential decision making, deep RL, imitation learning, generative modeling, and robotics; M.Sc./Ph.D. preferred with top conference publications.
Develops state-of-the-art generative models like diffusion and flow-matching for autonomous planning in self-driving tech. Requires PhD or MSc with 2-3 years experience in generative modeling for robotics, strong Python/C++ skills, and top research publications.
Develop and scale generative models like diffusion and flow-matching for autonomous driving plan generation. Collaborate across teams to productize models for real-world deployment, requiring PhD/MSc + 3+ years in generative modeling and strong Python/C++ skills.
Senior ML Research Scientist advances end-to-end ML models for autonomous driving, processing sensor data to behaviors using large-scale datasets and compute. Requires MS/PhD, 3+ years ML experience, Python expertise, and top-tier research publications.
Develops novel speculator algorithms and fine-tunes LLMs for production deployment, bridging research and customer needs with focus on data curation, hyperparameter tuning, and checkpoint evaluation. Requires Python, PyTorch, and experience with GPU clusters.
Develops and optimizes GPU-accelerated kernels and algorithms for ML/AI applications, co-designing with modeling, hardware, and software teams. Requires strong GPU programming expertise in CUDA/Triton and knowledge of ML models.
PhD intern researches and develops techniques to adapt LLMs and AI systems for enterprise domains, including method design, evaluation, and efficient post-training. Requires deep learning proficiency, PyTorch skills, and ongoing PhD studies.
Builds distributed training, inference, and data systems for frontier coding models, working with researchers to enable fast iteration. Requires strong infrastructure background and intuitions about language models.
Designs evaluation measures, harnesses, and datasets to assess risks from frontier AI systems, including dangerous capabilities testing. Collaborates with agencies, publishes methodologies for policymakers; requires 3+ years ML experience and publications in generative AI.
Conducts research in applied category theory to formalize semantics of probabilistic DSLs and develop models for real-world phenomena like relationships, dynamical systems, and natural language. Requires PhD-level expertise in category theory and functional programming skills.
Designs methods, systems, and experiments for AI controls and monitoring to ensure alignment in high-stakes environments, including real-time tracking, fail-safes, and red-team simulations. Requires 3+ years ML experience, published research in generative AI, and strong prototyping skills.
Research Scientist focuses on agent robustness, developing tests, exploits, and mitigations for safe AI agents. Requires 3+ years ML experience, RL techniques like RLHF/DPO, and published research in generative AI.
Conducts research on post-training methodologies and performant inference for AI models, balancing pure research with applied work for production systems. Requires PhD in ML with top publications and ability to design rigorous experiments at scale.
Conducts research on efficient AI systems focusing on real-time adaptation, model efficiency, and cross-stack optimization. Requires PhD or equivalent, 4-5+ years industry experience, and deep ML expertise including PyTorch/JAX and optimization techniques.
Conduct research and build foundational ML models for structured and tabular data, combining statistical learning theory, probabilistic modeling, and large-scale systems. Requires a PhD and strong experience in tabular/relational ML.
Deploys high-performance vision and multimodal AI models to robotic platforms, optimizing for low-latency inference under hardware constraints. Requires 3+ years in real-time robotics systems, expertise in PyTorch, C++/Rust, and edge deployment.
Develops RLHF and post-training methods for personalized, multimodal AI systems on consumer devices, focusing on reward modeling, preference learning, long-horizon evaluation, and alignment with user values. Requires strong ML research background in RLHF and related areas.
Builds macroeconomic models and scenario-based forecasting tools for transformative AI impacts on growth, labor markets, and income distribution. Requires PhD in Economics, expertise in macro modeling, computational methods, and grounding in real-world AI usage data.
Part-time AI Trainer annotates mortgage conversation data, provides feedback on AI responses, and tests system quality for mortgage servicing calls. Requires 5+ years mortgage customer service experience and industry knowledge.
Designs and implements mitigation stacks to prevent loss of control risks in frontier AI models, including prevention, monitoring, detection, and enforcement. Requires expertise in deep learning, transformers, PyTorch/TensorFlow, and AI safety research.
Senior Applied Researcher develops AI/ML solutions for healthcare challenges, applying GenAI, LLMs, and techniques like RAG to build production-ready models in SaaS environments. Requires Master's in relevant field, healthcare data experience, Python proficiency, and ML frameworks expertise.
Leads strategy and execution for measuring AI model capabilities across training and deployment, developing novel long-horizon evaluations and leading a team of researchers. Requires experience in LLM evaluations, technical leadership, and cross-team collaboration in fast-paced AI research environments.
Conducts advanced research in AI, machine learning, or related fields, developing novel algorithms and publishing results. Requires PhD and strong publication record.
Conducts core research on multimodal foundation models for world-simulations, bridging modeling, data, systems, and evaluation. Requires advanced degree in CS/ML, first-principles scaling intuition, and experience with large-scale GPU training.
Develops bi-directional/recurrent architectures for hierarchical sensorimotor perception inspired by brain principles to advance AGI. Requires PhD-level expertise in deep learning frameworks like PyTorch/JAX, theoretical rigor, and passion for biological intelligence.
Conducts fundamental research on hierarchical RL agents, developing architectures for world modeling, planning, and ethologically constrained learning. Collaborates cross-functionally to translate brain-inspired principles into scalable AGI systems; requires PhD-level expertise in AI/ML and strong coding skills.
Conducts fundamental research on brain-inspired world models and AGI architectures, designing experiments on cognition, causal representations, and unsupervised learning. Collaborates with neuroscientists and engineers; requires PhD-level expertise in deep learning frameworks like PyTorch/JAX.
Conducts fundamental research on neuroscience-inspired energy-based models and hierarchical latent variable models for AGI. Designs experiments on cognition and collaborates cross-functionally to build scalable systems. Requires PhD-level expertise in deep learning and PyTorch/JAX.
Develops AI/ML models to transform sensor data into personalized health and sleep insights, focusing on thermoregulation, foundation models, and behavioral simulations using vast sleep datasets. Requires PhD in ML/AI-related field, 3+ years practical ML experience, and strong publications.
Leads R&D on small language models and AI training, developing efficient architectures, optimizing performance, and ensuring safety. Collaborates with research, engineering, and product teams using Python, PyTorch, TensorFlow, or JAX.
Build state-of-the-art ML models to predict traffic behavior for autonomous driving, using generative sequence modeling and controllable agents for planning and simulation. Requires PhD preferred, 2+ years deploying ML systems, and expertise in PyTorch and robotics ML.
Develops efficient inference engines and RL/post-training pipelines for production-scale LLMs, optimizing algorithms, systems, and performance across the stack. Requires 3+ years in ML systems/RL/inference and advanced degree.
Collaborate on high-impact AI research projects, design and review deep learning models in PyTorch, analyze GPU performance, and co-author publications. Requires PhD in CS/AI/ML and hands-on expertise with Transformers, CNNs, and diffusion models.
University students contribute to generative AI projects like model training and failure mode identification, participate in focus groups, and collaborate on team challenges. Requires STEM enrollment, coding proficiency, and competitive programming experience; fully remote with flexible hours.
Conducts cutting-edge research on data for state-of-the-art AI agents like browser and SWE agents, develops prototypes using LLMs and frameworks like PyTorch/JAX, and publishes in top ML venues. Requires 3+ years ML experience and strong cross-functional communication.
Develops next-gen Agent RL training platform for enterprise GenAI, integrating cutting-edge research to train state-of-the-art models for complex use cases. Requires 5+ years LLM production experience, RLHF expertise, recent top publications, and advanced CS degree.
Develops and deploys state-of-the-art ML models and agents for enterprise GenAI using RL training and post-training algorithms. Requires 1-3 years LLM production experience, RLHF expertise, recent top publications, and advanced CS degree.
Develops and optimizes post-training algorithms for agent RL platforms, focusing on LLM training, inference frameworks, and multi-agent systems. Requires 1-3 years production LLM experience, expertise in PyTorch/CUDA, RLHF/PPO, and advanced degree.
Develops next-generation large language models through research, experimentation, and engineering on pre-training team. Requires strong Python/PyTorch skills, ML expertise, and MS/PhD in related field.
Research Engineer optimizes and scales production pretraining of frontier AI models, handling performance, debugging, experiments, and on-call incidents. Requires expertise in JAX, TPU, PyTorch, or large-scale ML systems with a 50/50 research-engineering balance.
Research Engineer implements and scales post-training techniques like Constitutional AI and RLHF for production AI models, optimizing capabilities, alignment, and safety. Requires strong Python skills, ML systems experience, and ability to handle complex distributed training pipelines.
Builds large-scale infrastructure for AI scientist training, evaluation, and deployment, resolving bottlenecks in distributed systems for scientific AGI. Requires 6+ years in infrastructure engineering with expertise in ML stacks, containers, and data pipelines.
Conducts experimental ML research on AI alignment and safety for powerful systems, focusing on scalable oversight, control, and stress-testing. Requires strong software/ML engineering, empirical research experience, and Python proficiency.
Builds next-generation training environments and evaluations for agentic AI models, blending research in reinforcement learning with robust engineering implementation. Requires strong technical judgment, agency, and experience in ML systems.
Conducts mechanistic interpretability research to reverse-engineer language models, developing methods to understand neural network algorithms for AI safety. Requires scientific research background, Python proficiency, and collaborative engineering mindset.
Research Engineer builds and optimizes reinforcement learning infrastructure for advancing AI capabilities like agentic models, tool use, and reasoning. Requires Python proficiency, ML frameworks experience, and ability to blend research with scalable engineering.
Research Engineer works end-to-end to remove bottlenecks toward scientific AGI, focusing on long-horizon reasoning, computer use, and model capabilities. Requires 8+ years ML experience, expertise in language model pipelines, distributed systems, and collaborative problem-solving.
Research Engineer focused on mechanistic interpretability, building tools and infrastructure to reverse-engineer neural networks for safer AI. Requires 5+ years software experience, Python proficiency, and AI research contributions.
Develops theories of intelligence grounded in neural network internal structures, focusing on belief geometries in LLMs and biological brains. Conducts experiments bridging mathematics, ML interpretability, and safety research; requires PhD-level quantitative depth and hands-on coding.
Leads research in database systems, focusing on query optimization, storage engines, indexing, and hardware-accelerated architectures. Collaborates with engineering to productize innovations and builds a research team. Requires PhD and 12+ years experience.
Develops scalable ML-based planning and prediction systems for autonomous driving trajectories. Requires expertise in sequential decision making, deep RL, imitation learning, generative modeling, and robotics; M.Sc./Ph.D. preferred with top conference publications.
Develops state-of-the-art generative models like diffusion and flow-matching for autonomous planning in self-driving tech. Requires PhD or MSc with 2-3 years experience in generative modeling for robotics, strong Python/C++ skills, and top research publications.
Develop and scale generative models like diffusion and flow-matching for autonomous driving plan generation. Collaborate across teams to productize models for real-world deployment, requiring PhD/MSc + 3+ years in generative modeling and strong Python/C++ skills.
Senior ML Research Scientist advances end-to-end ML models for autonomous driving, processing sensor data to behaviors using large-scale datasets and compute. Requires MS/PhD, 3+ years ML experience, Python expertise, and top-tier research publications.
Develops novel speculator algorithms and fine-tunes LLMs for production deployment, bridging research and customer needs with focus on data curation, hyperparameter tuning, and checkpoint evaluation. Requires Python, PyTorch, and experience with GPU clusters.
Develops and optimizes GPU-accelerated kernels and algorithms for ML/AI applications, co-designing with modeling, hardware, and software teams. Requires strong GPU programming expertise in CUDA/Triton and knowledge of ML models.
PhD intern researches and develops techniques to adapt LLMs and AI systems for enterprise domains, including method design, evaluation, and efficient post-training. Requires deep learning proficiency, PyTorch skills, and ongoing PhD studies.
Builds distributed training, inference, and data systems for frontier coding models, working with researchers to enable fast iteration. Requires strong infrastructure background and intuitions about language models.