Leads the research agenda for humanoid robotics, developing foundation-model and reinforcement-learning methods for dexterous manipulation and deploying them on real robotic systems. Requires a PhD, strong robotics research publications, and senior-level technical leadership.

The Research Engineer will apply advances in agents and language models to build and evaluate multi-agent systems for automated code validation and review. The role requires a computer science or equivalent background, research experience, strong programming skills, and product intuition.

Leads Deepgram’s end-to-end TTS research program, setting technical direction, training and evaluating large-scale speech-generation models, and turning breakthroughs into production systems. The role combines hands-on technical leadership with building and developing a high-performing research organization.

Conduct field research and human factors analysis with pilots, operators, and customers to shape collaborative work requirements, mission autonomy design, training, and readiness assessments. The role requires human-machine teaming expertise, operational research experience, and comfort working in field and test environments.

Conducts applied human-machine teaming experimentation for autonomous systems, testing collaboration robustness, operator performance, and failure limits. Requires a relevant degree, human participants research experience, experimental and multivariate analysis skills, and eligibility for U.S. DoD security clearances.

Designs scalable, traceable operator-autonomy relationships for mission autonomy systems by translating human-machine teaming research into requirements, design patterns, and evaluation methods. The role requires expertise in human factors, cognitive systems, experimentation, multivariate analysis, and multidisciplinary systems engineering.

Conducts frontier AI research for health, developing and evaluating scalable training methods, models, and agents that improve medical reasoning, reliability, and real-world outcomes. Requires exceptional machine learning or biomedical AI research depth, hands-on coding and experimentation, and end-to-end ownership of ambiguous problems.

Build Vanta’s organizational intelligence layer by shipping prototypes, internal tools, and AI agent workflows that make cross-source data useful to EPD, GTM, and other teams. The role requires recent hands-on LLM product work, independent problem scoping, and strong judgment around AI quality, reliability, cost, and latency.

Research Scientist defining and executing research on reliable long-horizon agents in enterprise environments. The role focuses on post-training and reinforcement learning, agent memory, evaluation, verification, and structured representations, combining hands-on experimentation with product delivery and publication.

Senior software engineer responsible for building AI agents, developer tooling, and automation that improve planning, coding, testing, code review, CI, and local development workflows. Requires 6–8 years of software engineering experience, hands-on agentic coding tool experience, and proficiency in Python or JavaScript/TypeScript, AWS, and PostgreSQL.

The Principal AI Engineer architects enterprise-grade AI/ML platforms and GenAI infrastructure, establishing standards for agentic systems, RAG, model serving, observability, security, and multi-cloud deployment. The role requires 8+ years in cloud architecture and platform engineering, including substantial AI/ML infrastructure experience.

Conduct speech technology research on text-to-speech, ASR, speech-to-speech, and speech analysis models during a six-month project. The role requires a relevant master's qualification or PhD study, neural-network development experience, and Python skills.

Build and ship agentic AI product experiences, internal automation, and customer-facing features across the stack. The role requires 5+ years of software engineering experience, hands-on experience with AI or LLM-powered products, Python proficiency, and strong autonomy.

Build and operate the autonomy stack for industrial robots, spanning task, behavior, motion, and trajectory planning. The role requires hands-on hardware experience, strong Python and C++ skills, and expertise in planning architectures, recovery, and multi-robot coordination.

Defines the technical vision and architecture for enterprise AI products, focusing on agentic reasoning, NL-to-SQL, RAG, reliability, and scalable LLM orchestration. Requires 10+ years of software engineering experience, substantial LLM deployment leadership, and Principal-level technical influence.

Evaluates model and Generative AI risks across Upstart Bank’s model inventory, conducting risk assessments, monitoring reviews, quantitative analyses, and governance activities. Requires a quantitative master’s degree, 4+ years of relevant experience, and coding skills in Python, R, or similar languages.

Researches and productionizes machine-learning models for session replay analysis, user behavior prediction, and synthetic testing. The role requires strong mathematics, PyTorch, systems programming in Rust/CUDA/C, and a product-oriented approach to shipping.

Research Engineer focused on designing benchmarks, evaluation systems, rubrics, and failure-analysis workflows for frontier language models. The role requires strong applied AI research and coding experience, with expertise in model evaluation, data quality, and backend systems.

Conducts causal inference research for financial market prediction and portfolio optimization, developing and validating models from research through live trading. Requires Ph.D.-level coursework, strong causal inference and statistics expertise, mathematical ability, and production Python skills.

Leads a large-scale program to map the primate brain and develop an openly accessible atlas integrating anatomy, connectivity, physiology, imaging, electrophysiology, and computational modeling. Requires deep primate neuroimaging expertise, multimodal data integration, and senior research leadership experience.

Conducts end-to-end experimental neuroscience research to read, write, and perturb working-memory representations in mouse cortex. The role combines rodent surgery and behavior, two-photon holographic optogenetics, and computational analysis of neural population data.

Develops a volumetric functional ultrasound imaging platform for whole-brain studies in awake primates, spanning experimental design, hardware collaboration, computational pipelines, and large-scale neuroscience analysis. Requires primate neuroimaging experience, strong Python or MATLAB skills, and expertise in image reconstruction, registration, and multimodal data analysis.

Develop computational and theoretical models of neural representations, dynamics, and control using large-scale recordings across subjects and species. The role combines machine learning, dynamical systems, experimental design, and NeuroAI architecture development, with opportunities to lead research and mentor collaborators.

Leads human intracranial neuroscience studies of language, mathematics, perception, and working memory, from cognitive paradigm design through analysis and open-data release. The role requires advanced electrophysiology, quantitative analysis, clinical research collaboration, and a PhD or MD/PhD.

Leads end-to-end experimental systems neuroscience programs studying perception, thought, and internal state through large-scale neural recording and causal perturbation experiments. The role requires multi-animal neurophysiology experience, quantitative analysis skills, and close collaboration with computational and engineering teams.

Conducts hands-on medicinal chemistry research to evaluate AI-generated molecules and synthetic routes, advancing small-molecule programs from design through experimental validation. Requires a chemistry PhD, sustained synthetic experience, and cross-functional collaboration skills.

Research Engineer developing and deploying machine-learning algorithms for autonomous driving and robotics systems. The role targets recent MS or PhD graduates with experience in areas such as foundation models, diffusion policies, reinforcement learning, computer vision, and robotics.

Research Engineer improving the capability, efficiency, and reliability of autonomous AI agents through benchmarks, experiments, prompting, model optimization, and post-training. The role requires 4+ years of ML/AI research experience, strong software engineering skills, and a master’s or Ph.D. in a relevant scientific field.

Leads frontier-scale pretraining research for multimodal image, video, and audio foundation models, shaping architectures, objectives, data strategies, and distributed systems. The role requires prior ownership of production-grade foundation-model pretraining, deep Python and PyTorch expertise, and strong experience with visual generative models.

Advances vision-language models and integrates multimodal capabilities with FLUX diffusion and flow pipelines. The role requires demonstrated VLM pretraining or substantial architectural advancement, strong research or production results, and multi-node distributed training experience.

Conduct research on long-horizon, multi-agent AI behavior by designing agent environments, analyzing large-scale data, and running experiments. The role requires strong research judgment, rapid execution, independence, and familiarity with current AI developments.

Build, optimize, and evaluate long-running and multi-agent AI systems, along with tools for monitoring and analyzing their real-world behavior. The role requires software engineering experience with coding agents, strong independence, and familiarity with current AI developments.

Designs AI-native teaching methods and establishes rigorous measurement of skill gain, retention, and transfer. The role requires deep learning-science knowledge, strong experimental and statistical skills, human-subjects research experience, and technical fluency with Python or R.

Builds data pipelines, distributed training infrastructure, simulation environments, and evaluation systems for multimodal world models and reinforcement-learning agents. The role partners with research scientists to scale experiments and turn prototypes into reliable observability and security products.

Build and optimize custom AI models for real-time anomaly detection across high-throughput security data. The role combines applied mathematics, model training, GPU and systems optimization, production deployment, interpretability, and ongoing model operations.

Leads a company-wide shift toward agentic engineering while remaining hands-on in software development, cloud architecture, prototyping, and technical ownership. The role requires extensive full-stack and SaaS experience, strong AI/LLM expertise, and the ability to influence global engineering teams.

Leads the strategy, architecture, governance, operations, and adoption of an enterprise AI platform. The role builds AI agents and integrations, manages model and platform lifecycle, and ensures secure, compliant, scalable, and cost-effective AI delivery.

Conduct research and build evaluation systems for autonomous AI agents operating on real engineering workflows. The role combines production experimentation, statistical measurement, automated optimization, and post-training open-weight vision-language models using proprietary autonomous-driving data.

Research and evaluate frontier AI capabilities for cybersecurity, rapidly prototyping tools, designing rigorous benchmarks, and helping operationalize reliable capabilities into products. Requires deep security expertise, strong technical communication, and at least seven years of relevant experience.

Develops novel Bayesian statistical and machine learning methods, production implementations, and evaluation frameworks for clinical AI. The role requires a PhD in statistics or machine learning, strong Python and PyTorch skills, and expertise in Bayesian modeling and deep learning.

Develops novel causal inference and treatment-effect modeling methods for clinical AI, translating research into production-ready code and evaluation frameworks. Requires a PhD in causality, statistics, or machine learning, strong Python and PyTorch skills, and experience with observational and randomized trial data.

Conducts research and develops production-ready survival analysis and machine learning methods for clinical AI, including evaluation frameworks and research publications. Requires a PhD in machine learning or statistics, strong statistical foundations, and expertise in Python and PyTorch.

Researcher developing evaluations, red-teaming pipelines, and novel mitigations for frontier AI safety risks. The role requires deep technical expertise, research engineering experience, advanced training in computer science or machine learning, and proficiency in Python or similar languages.

Research Scientist developing and deploying machine learning models for fraud detection, identity verification, and financial risk. The role targets new PhD graduates or early-career researchers with strong quantitative foundations, Python experience, and interest in owning the full production ML lifecycle.

Conducts machine learning research for web search by designing transformer architectures, training embedding models, building large-scale datasets, and developing evaluation systems. Requires graduate-level ML experience or exceptional undergraduate ability and strong PyTorch skills.

Leads applied AI innovation for authentication and security products, taking ideas from research through prototypes and production recommendations. The role requires 8+ years in ML or applied research, hands-on delivery of 0-to-1 AI systems, and strong Python, ML, and software engineering skills.

Conduct strategic, technically rigorous research to anticipate and mitigate loss-of-control risks from increasingly capable AI systems, including recursive self-improvement. The role combines hypothesis-driven research, rapid prototyping, safety evaluations, monitoring, and institutionalizing effective interventions.

Leads the architecture, development, integration, and lifecycle evolution of autonomy and motion-planning software for unmanned platforms across multiple operational domains. The role requires production C++ expertise, 7+ years of relevant experience, technical leadership, and eligibility for a U.S. Secret clearance.

Owns mid-training for LLMs, optimizing data mixes, synthetic data pipelines, annealing schedules, and context extension to enhance reasoning, coding, and math capabilities for AI agents. Requires deep LLM pipeline expertise, hands-on large model training, and original research contributions.

Conducts research to build end-to-end AI software agents capable of reasoning on real-world tasks, contributing to products like Devin and Windsurf. Requires expertise in applied AI and machine learning.