Researcher, Artifacts - Agent Post-Training
As a member of Agent Post-Training, Artifacts, the role involves training frontier models to produce polished, useful work products such as documents, spreadsheets, slide decks, dashboards, reports, analyses, and other interactive or editable artifacts. Responsibilities include designing and running experiments to improve agentic model behavior for complex software and plugins, owning end-to-end improvements to the post-training stack including reinforcement learning, data pipelines, graders, reward signals, evaluations, diagnostics, and model-behavior analysis. The role involves building evaluations and environments to identify new model failures and converting these failures into training data, product fixes, or new research paths. Collaboration with Codex and ChatGPT product teams to translate product signals into model improvements is required. Other duties include working on early-training and alignment interventions, deciding integration and capability readiness for major model runs, improving machinery for large-scale training and launch regarding experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness, and undertaking cross-functional projects that involve model training, product infrastructure, and production agent systems. Debugging hard failures in shipped or near-shipped models and transforming qualitative behaviors into hypotheses, experiments, and fixes is also part of the role.
Applied AI Researcher, Multi-Agent Systems
The Multi-Agent Systems team focuses on designing architectures in which multiple agents coordinate to solve problems that require structured interaction across multiple reasoning processes. Researchers build systems that structure communication, route information, and coordinate decision-making across agents operating with different views of the problem. Researchers investigate the interaction patterns that govern how agents collaborate, studying how agents exchange information, critique and refine each other’s reasoning, and coordinate execution across complex workflows. Their work identifies the mechanics behind effective communication, delegation, and coordination, establishing the design language for how systems of agents can operate as cohesive, high-performing teams, with capabilities that arise from interaction rather than individual performance.
Research Scientist, Safety Post Training
The role involves owning the production outcome and taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. It includes ensuring full-stack integrity by overseeing the end-to-end health of the platform and seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment. Responsibilities also cover scaling the feedback loop by building automated systems to monitor model performance and data drift across geographically dispersed environments for appropriate reliability. Managing the technical lifecycle within diverse regulatory frameworks and leading the response for production issues in mission-critical environments to ensure rapid resolution and prevent recurrence are also required. Additionally, the role entails translating deep technical performance metrics into clear insights for senior international government officials and partnering with Engineering and ML teams to influence the technical architecture and decisions of future use cases based on lessons learned in the field.
Research Scientist (Singapore)
Drive foundational research on video generation models, taking ownership across the full research cycle and driving post-training research. Collaborate closely with data, infrastructure, and adjacent modeling teams to translate research findings into durable model improvements. Build and maintain scalable systems for ingesting, preprocessing, and delivering large-scale video data for model training. Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes. Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs. Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems. Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency. Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns. Build tooling to support deduplication workflows at scale, including near-dedup pipelines over large video corpora. Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance distillation and adversarial distillation, focusing on preserving or improving generation quality while reducing inference cost. Develop reward models and preference-based fine-tuning pipelines that align video generation quality with human judgments across aesthetics, motion quality, and prompt adherence. Analyze the relationship between base model behavior and post-training outcomes, working with foundation model team to inform pretraining decisions accordingly.
Researcher, Alignment Oversight
As a researcher on the Alignment Oversight team, you will design and run experiments to improve oversight of increasingly capable AI models, involving model training, evaluation design, and research infrastructure. Responsibilities include deploying practical systems for action monitoring, red-teaming, and human-in-the-loop control; developing evaluations for alignment failure modes of frontier models, such as overeagerness and instruction following failures; analyzing deployment data to understand model failures and oversight gaps; developing techniques to feed oversight signals back into training while preserving oversight reliability; producing publishable research advancing alignment science; collaborating with research, product, security, safety, and engineering teams to implement alignment ideas; and rapidly moving from research intuition to working experiments, prototypes, and evidence that inform future model improvements.
Robotics Research Intern, Robot Learning (Summer 2026) | PhD Internship
As a research intern in Robot Learning at Field AI, you will work closely with researchers and engineers to explore novel approaches to robot learning and autonomy, focusing on scalable methods that generalize across tasks and embodiments. You will design experiments, develop learning pipelines, and validate ideas on real robotic platforms. Responsibilities include developing a multi-modal data collection platform for day/night robot navigation data collection, collecting high-quality datasets for reproducible and comparable research and evaluation, and summarizing and publishing findings in high-quality robot research conferences or journals. You will work independently while collaborating effectively in a research environment, contributing directly to FieldAI's deployed autonomy stack.
Staff AI Scientist
Lead applied research and development for the models and datasets at the core of Fiddler's Trust Service and suite of guardrail classifiers and evaluators that customers depend on to keep their LLM and agentic applications safe, accurate, and compliant in production. Partner closely with other engineering teams, Product, and Customer Success to build strong relationships with customer data science and ML engineering teams, supporting their AI observability journey and ensuring measurable value from Fiddler. Design, train, and ship production classifiers for safety, security, and quality detection under strict latency and cost constraints. Lead the development of synthetic and adversarial dataset pipelines, including novel methods for generating, filtering, and validating data that exposes failure modes. Drive the technical direction of generative insights, including the LLM- and agent-powered analysis layer that helps customers diagnose issues in their AI applications. Contribute to evaluation and experimentation infrastructure to reliably measure model quality, regression, and drift. Explore reinforcement learning and preference-based methods where applicable. Collaborate with Backend and Platform engineers to transition research prototypes into hardened, scaled, and observable services. Partner with Product, Solutions Engineering, and Customer Success to translate enterprise customer needs into research problems and research results back into product. Mentor AI Scientists, raise the technical bar through code and design review, and represent Fiddler externally through publications, talks, or open-source contributions when appropriate.
Principal AI Researcher
Define and lead the AI research agenda focused on clinical trust, safety, evidence-based AI, and non-deterministic system design. Partner with product teams on architectural decisions by providing a research perspective and propose enhancements to existing architectures. Provide technical guidance to product engineers in implementing those enhancements. Publish original research and contribute to the clinical AI academic community. Represent Tortus at conferences and with clinical and regulatory stakeholders. Contribute to the Class IIa medical device submission and set the technical bar for AI hiring as the team scales.
Founding Research Scientist, Human Simulation
Lead the simulation initiative to develop a model that predicts human thoughts, wants, and decisions. Develop the human preference model trained on millions of real human conversations to simulate human behavior at scale. Model how people react to new ideas, make decisions, how preferences shape markets, and how change ripples through society. Work on multi-agent dynamics to simulate cohorts of synthetic humans deliberating, reaching consensus, or splitting into camps. Implement generalization and active learning by learning from patterns across people, contexts, and questions and updating the model with real human input when uncertain. Define research problems, scope research programs, and decide success criteria. Train models, write evaluations, and collaborate with research engineers to put the model into production. Communicate complex research ideas and share roadmap and vision in writing.
Principal Applied AI Researcher - Domain- Specific Models (India)
The Principal Applied AI Researcher is responsible for setting the company-level technical direction for domain-specific model strategy, defining how Articul8 builds, evaluates, scales, and sustains model superiority across continued pre-training, fine-tuning, post-training, and release quality standards. They architect the agentic model development paradigm by designing the agent-orchestrated research infrastructure to enhance research capabilities. The role involves leading deep research on model adaptation methodology, data curation strategies, post-training methods, and training dynamics while deploying agentic systems for exhaustive studies and failure analyses. Additionally, they shape model strategy across all domains and verticals of the company, prioritizing new model domains through agent-driven competitive intelligence and market analysis. They define evaluation strategy, including benchmark design, expert assessments, model failure analysis, and robustness standards, building always-on evaluation systems. The researcher leads cross-cutting research initiatives to strengthen the model layer, influences platform-level decisions about model lifecycle management, portfolio strategy, release criteria, and integration architecture. They mentor senior researchers, coach on agent-augmented research design, and raise technical judgment and rigor. Lastly, they maintain hands-on research impact through publications, patents, and visible output, exemplifying the use of massively parallel agentic systems for groundbreaking research.
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