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.

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.

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.

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.

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.

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.

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.

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.

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.

Develops and optimizes AI and LLM inference kernels for Quadric’s neural processing platform, profiling performance across hardware configurations and improving compiler and runtime components. Requires 5+ years of kernel optimization experience, strong C/C++ and Python skills, and familiarity with CUDA, DSP, NEON, or Triton.

Conducts cutting-edge research in Generative AI, building foundation models and autonomous agents for cloud observability, SRE, and code repair. Requires PhD in ML or related field, publications at top conferences, and expertise in PyTorch/TensorFlow distributed training.

Leads the technical vision and implementation of Docker’s containerized AI agent platform, including runtime infrastructure, distributed systems, evaluation, and operational excellence. The role requires 10+ years of software engineering experience, principal-level technical leadership, and practical experience with Go, Docker, and LLM-based agent development.

Conduct cutting-edge research on Large Language Models, focusing on transformer optimization, distributed training, data curation, and RL. Collaborate on experiments, deploy models to production, and drive voice AI innovations.

Pioneers Latent Space Models to solve core challenges in voice AI, developing neural audio codecs, generative speech models, and scalable multimodal systems. Requires strong expertise in statistical learning, foundation models, and bridging theory to efficient deployment.

Develops and customizes large language and deep learning models for customer-specific applications, owning training, fine-tuning, evaluation, and agentic-system development. Requires advanced graduate education, hands-on experience with 1B+ parameter models, Python, PyTorch, and distributed training.

Develops novel C++ computer vision algorithms and real-time perception pipelines for autonomous platforms. The role requires strong computer vision, image processing, machine learning, and high-performance systems expertise, with deep learning and robotics experience advantageous.

Develops novel reinforcement learning techniques using synthetic environments and feedback to enhance large-scale AI models. Designs experiments, analyzes dynamics, and integrates research into production systems; requires strong RL/ML background and engineering skills.

Research Engineer develops and experiments with multimodal world models, focusing on data strategies, training techniques, evaluations, and production deployment for AI simulation technologies. Requires 4+ years in ML research/engineering and proficiency in PyTorch or JAX.

Founding ML Researcher shapes ML research direction for document AI, owns end-to-end lifecycle from research to production deployment. Requires expertise in VLMs, computer vision, unstructured data parsing; PhD preferred.

Leads open-source ML research by designing experiments, developing evaluation methodologies, analyzing preference data, and releasing datasets/code to advance AI model transparency. Requires PhD-level expertise in ML/LLMs and hands-on experience with RLHF/DPO fine-tuning.

Designs and conducts experiments to evaluate AI models using human preference data, develops new metrics and methodologies, and analyzes large-scale interaction data to advance model reliability and alignment.

Research and develop improvements to pre-trained models for deployment in ChatGPT and API using reinforcement learning and product-driven approaches. Requires strong ML engineering, research experience with novel models, and ability to debug large codebases.

Pioneers innovative ML techniques and builds foundation models for clinical information extraction and synthesis from medical records. Requires PhD in CS/math with NLP/ML focus, high-impact publications, and experience with large-scale model training using PyTorch/JAX.

Researches, develops, evaluates, and optimizes machine learning models across the full research lifecycle, with a focus on novel methods, scalable training, and efficient inference. Requires strong Python, software engineering, machine learning fundamentals, and experience with GPUs or TPUs and distributed training.

Lead AI research defining the agenda for trustworthy agentic systems, memory, retrieval, and evaluation frameworks. PhD required with 5+ years focused on LLMs/agents and a strong publication record; work ships directly to production for high-stakes financial users.

Conducts research and builds benchmarks, methods, and infrastructure to evaluate the capabilities and progress of large language models. The role requires strong software engineering, prototyping, data-quality review, and rigorous measurement skills.

Leads end-to-end applied ML research in NLP, LLMs, retrieval, and multimodal models for healthcare AI, driving from experimentation to production deployment with rigorous evaluation and clinician collaboration. Requires 7+ years experience, MS/PhD, and depth in ML areas like PyTorch tooling.

Applied AI Researcher on System Discovery team explores new AI system architectures, prototypes human-AI collaborations, and draws cross-domain insights to redefine enterprise workflows. Requires proven research track record, expertise in compound AI systems and agentic techniques, strong programming for rapid prototyping.

Designs self-constructing AI systems that autonomously generate, assemble, and refine subsystems using meta-learning, program synthesis, and evolutionary methods. Requires proven research in modular AI architectures, daily AI tool usage, and strong prototyping skills.

Designs feedback-driven AI architectures for system self-improvement, enabling autonomous evolution through reflection, retraining, and adaptation. Requires proven research in compound AI systems, daily AI tool usage, and strong prototyping skills.

Designs and researches compound AI systems integrating LLMs, agents, and execution components for reliable enterprise workflows. Requires proven AI research track record, daily AI tool usage, strong prototyping skills, and expertise in agentic techniques.

Develops and evaluates post-training techniques like supervised fine-tuning, RLHF/DPO, and continual adaptation to align foundation models with enterprise systems. Requires expertise in adapting LLMs/SLMs, compound AI systems, and strong prototyping skills.

Designs and constructs AI benchmarks and evaluation frameworks to measure reasoning, reliability, and real-world impact of intelligent systems. Requires experience with model evaluations, statistical rigor, building with AI models, and strong programming for prototypes.

AI/ML research intern building prototypes for sleep fitness applications like multimodal activity understanding, time-series forecasting, and personalized thermoregulation. Works with cross-functional team on data-driven solutions; requires student status in CS/data science/ML-related field, prefers research experience and ML proficiency.

Develops evaluation methods, alignment techniques, and adversarial testing for large language models to ensure safety and alignment with human values. Requires PhD in ML/CS, production code skills, GPU experience, and transformers/RL expertise.

Advances AI products through post-training SOTA LLMs using supervised and reinforcement learning techniques on rich query datasets. Owns data pipelines, training frameworks, and model integration while collaborating across teams. Requires 2-6+ years in large-scale LLMs and Python/PyTorch expertise; PhD preferred.

Researches and engineers code-generating LLMs and autonomous agent systems for enterprise automation. The role requires deep code-model expertise, strong Python and deep-learning framework skills, scalable systems experience, and a PhD with relevant publications.

AI Research Scientist explores novel LLM modeling, NLP, and reasoning approaches for healthcare applications, prototypes capabilities, and deploys them into production with engineering and product teams. Requires strong ML/NLP background with publications and hands-on deep learning experience.

Designs long-term memory architectures for LLMs, builds multi-type memory systems, researches agent memory sharing and context management, and runs evaluations. Requires deep LLM/retrieval expertise and impactful research track record.

Leads hands-on development of AI-native, distributed systems that integrate LLMs, retrieval, and graph intelligence into enterprise IT workflows. Requires 7+ years of software engineering experience, strong architecture and API expertise, modern programming skills, and experience mentoring engineers.

Conducts advanced LLM research for post-training, roleplay, and real-time integration into an anime-style 3D adventure game AI companion. Requires deep LLM expertise, anime passion, and ability to build scalable infrastructure on a small onsite team.

Researches and develops motion generation models (text-to-motion, audio-to-motion, facial animation) for real-time AI anime companions in narrative-driven 3D games. Requires hands-on AI model training experience and anime passion; on-site in SF or Tokyo.

Designs and scales infrastructure for evaluating multimodal generative AI models, including pipelines, metrics, and integration into training loops. Requires 5+ years in ML evaluation systems, expertise in visual data, Python, and ML frameworks like PyTorch.

Advances AI coding models through research, experimentation, and system optimization on the Codex team. Collaborates to improve code generation, reasoning, and performance for real-world deployment.

Develops scalable voice interface features through prototyping, infrastructure building, and R&D. Requires PhD in ML or related field, top conference publications, Python/LLM fluency, and strong engineering skills.

Develops controllability, personalization, and productization for video foundation models using fine-tuning, RL, and evaluation techniques. Requires deep expertise in visual generative models, PyTorch, and product-focused research for creative workflows.

Leads research on post-training data curation for foundation models, designing algorithms to generate/improve instruction and preference datasets, and unifying pre/post-training optimization. Requires 3+ years deep learning research, post-training experience with vision/language/multimodal models, and PyTorch proficiency.

Conducts machine learning research in medical NLP for conversation summarization, evidence extraction, and outcome prediction. Publishes at top AI conferences, deploys models to production, and requires MS/PhD plus strong PyTorch/TensorFlow experience.