Leads development of models and algorithms for real-time voice AI agents, focusing on speech understanding, naturalness, and production deployment. Requires 8+ years in AI/ML engineering, Python fluency, and experience deploying LLMs.
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.
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 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.
Leads AI platform development as a hands-on technical authority, owning GCP system design, event-driven data pipelines, AI integrations with LLMs, and observability for healthcare data. Requires 12+ years engineering experience, 5+ as Tech Lead, expert in Go/Python/TypeScript and distributed systems.
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.
Develop techniques to predict and mitigate unsafe behaviors in early-stage base models, design safer pretraining architectures, and integrate safety signals throughout training. Collaborate across safety teams to build robust, scalable safety foundations grounded in real-world risks.
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.
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.
Develops advanced systems for human-in-the-loop AI data alignment using RLHF/DPO, improves data quality, and builds AI-assisted labeling tools. Requires Master's/PhD, 3+ years ML experience, Python/PyTorch proficiency, and top-tier 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.
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.
ML Research Engineer trains foundational embedding models for web search, designs novel transformer architectures, creates datasets and evals, and beats state-of-the-art performance. Requires graduate-level ML expertise and ability to implement transformers in PyTorch.
Develops embedding models and retrieval systems to enable frontier AI models to access relevant information dynamically. Requires deep expertise in representation learning, vector retrieval, and transformer LLMs, with experience scaling ML systems.
Conducts research in mechanistic interpretability to understand deep network representations and enhance AI safety. Requires PhD or equivalent in ML/CS, 2+ years research engineering with Python, and passion for safe AGI.
Advances AI capabilities through cutting-edge reinforcement learning research, training intelligent agents for models like o1 and o3. Requires RL research background, strong coding skills, and ability to iterate quickly in a fast-paced environment.
Designs and tests zero-knowledge proof systems for biometric likeness verification in privacy-focused identity solutions. Requires expertise in zk-SNARKs/STARKs, cryptography, and prototyping tools like Circom and Semaphore.
Builds ambitious RL environments and evaluation systems to measure and steer frontier AI models toward safe AGI. Requires strong ML research engineering, statistical skills, and red-teaming mindset for end-to-end project ownership in fast-paced setting.
Builds advanced AI systems using massive-scale distributed machine learning to achieve breakthrough performance. Requires strong programming skills and experience with large distributed systems.
We are seeking full-time researchers to develop and analyze learning algorithms for a system that represents domain knowledge as modular probabilistic models. The role involves theoretical problems with immediate implementation applicability in finance and scientific research.
Develops safe and reliable AI models for healthcare applications using techniques like RLHF, automated red teaming, and scalable oversight. Requires PhD, 4+ years in deep learning/LLM research, and focus on practical AI safety improvements.
Conducts research on societal impacts of AI models, translates policy problems into technical evaluations, and develops interventions for safe AGI deployment. Requires 3+ years research experience, Python proficiency, and expertise in AI safety techniques like RLHF.
Develops advanced AI agents for healthcare revenue recovery, focusing on human-like conversational AI, model improvements, LLM orchestration, and evaluation frameworks to handle insurance interactions and billing tasks.
Develops computational operators to enable LLMs to perform transparent, verifiable reasoning over thousands of iterations on knowledge states like scientific papers. Requires LLM experience, reasoning intuitions, and strong software engineering skills.
Designs and develops cutting-edge multimodal AI systems integrating text, speech, and vision. Conducts research on representation learning using Python, JAX, PyTorch, TensorFlow, with expertise in distributed training and autoregressive models.
Designs and implements experiments for AI alignment research, focusing on scalable solutions for human intent following, risk evaluation, model robustness, and oversight methods in complex scenarios. Requires PhD-level research experience and strong ML engineering skills.
Designs, implements, and maintains complex AI/NLP models for the Verneek AI platform, focusing on fundamental research grounded in real-world applications. Requires MSc/PhD in AI/NLP, 4+ years Python/PyTorch experience, and strong AI fundamentals.
Leads development of models and algorithms for real-time voice AI agents, focusing on speech understanding, naturalness, and production deployment. Requires 8+ years in AI/ML engineering, Python fluency, and experience deploying LLMs.
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.
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 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.
Leads AI platform development as a hands-on technical authority, owning GCP system design, event-driven data pipelines, AI integrations with LLMs, and observability for healthcare data. Requires 12+ years engineering experience, 5+ as Tech Lead, expert in Go/Python/TypeScript and distributed systems.
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.
Develop techniques to predict and mitigate unsafe behaviors in early-stage base models, design safer pretraining architectures, and integrate safety signals throughout training. Collaborate across safety teams to build robust, scalable safety foundations grounded in real-world risks.
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.
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.
Develops advanced systems for human-in-the-loop AI data alignment using RLHF/DPO, improves data quality, and builds AI-assisted labeling tools. Requires Master's/PhD, 3+ years ML experience, Python/PyTorch proficiency, and top-tier 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.
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.
ML Research Engineer trains foundational embedding models for web search, designs novel transformer architectures, creates datasets and evals, and beats state-of-the-art performance. Requires graduate-level ML expertise and ability to implement transformers in PyTorch.
Develops embedding models and retrieval systems to enable frontier AI models to access relevant information dynamically. Requires deep expertise in representation learning, vector retrieval, and transformer LLMs, with experience scaling ML systems.
Conducts research in mechanistic interpretability to understand deep network representations and enhance AI safety. Requires PhD or equivalent in ML/CS, 2+ years research engineering with Python, and passion for safe AGI.
Advances AI capabilities through cutting-edge reinforcement learning research, training intelligent agents for models like o1 and o3. Requires RL research background, strong coding skills, and ability to iterate quickly in a fast-paced environment.
Designs and tests zero-knowledge proof systems for biometric likeness verification in privacy-focused identity solutions. Requires expertise in zk-SNARKs/STARKs, cryptography, and prototyping tools like Circom and Semaphore.
Builds ambitious RL environments and evaluation systems to measure and steer frontier AI models toward safe AGI. Requires strong ML research engineering, statistical skills, and red-teaming mindset for end-to-end project ownership in fast-paced setting.
Builds advanced AI systems using massive-scale distributed machine learning to achieve breakthrough performance. Requires strong programming skills and experience with large distributed systems.
We are seeking full-time researchers to develop and analyze learning algorithms for a system that represents domain knowledge as modular probabilistic models. The role involves theoretical problems with immediate implementation applicability in finance and scientific research.
Develops safe and reliable AI models for healthcare applications using techniques like RLHF, automated red teaming, and scalable oversight. Requires PhD, 4+ years in deep learning/LLM research, and focus on practical AI safety improvements.
Conducts research on societal impacts of AI models, translates policy problems into technical evaluations, and develops interventions for safe AGI deployment. Requires 3+ years research experience, Python proficiency, and expertise in AI safety techniques like RLHF.
Develops advanced AI agents for healthcare revenue recovery, focusing on human-like conversational AI, model improvements, LLM orchestration, and evaluation frameworks to handle insurance interactions and billing tasks.
Develops computational operators to enable LLMs to perform transparent, verifiable reasoning over thousands of iterations on knowledge states like scientific papers. Requires LLM experience, reasoning intuitions, and strong software engineering skills.
Designs and develops cutting-edge multimodal AI systems integrating text, speech, and vision. Conducts research on representation learning using Python, JAX, PyTorch, TensorFlow, with expertise in distributed training and autoregressive models.
Designs and implements experiments for AI alignment research, focusing on scalable solutions for human intent following, risk evaluation, model robustness, and oversight methods in complex scenarios. Requires PhD-level research experience and strong ML engineering skills.
Designs, implements, and maintains complex AI/NLP models for the Verneek AI platform, focusing on fundamental research grounded in real-world applications. Requires MSc/PhD in AI/NLP, 4+ years Python/PyTorch experience, and strong AI fundamentals.