AI Applied Research Scientist Jobs

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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.

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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.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house, developing and extending state-of-the-art methods in uncertainty quantification and uncertainty calibration. This includes understanding the AI systems built by the company, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate across several teams with different backgrounds.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built by the company, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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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.

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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.

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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.

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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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The Principal Applied AI Researcher is responsible for setting the company-level technical direction for domain-specific model strategy, defining how models are built, evaluated, scaled, and sustained across continued pre-training, fine-tuning, post-training, and release quality standards. They architect the agentic model development paradigm by designing research infrastructure such as experiment orchestration, data pipeline automation, continuous evaluation, and competitive benchmarking to enhance research productivity. They lead deep research on model adaptation methodology, data curation, post-training methods, and training dynamics using agentic systems for parallel experiments and failure analysis. They also shape model strategy across all company domains by prioritizing new model domains and using agent-driven competitive intelligence and market analysis. The role includes defining evaluation strategies involving benchmark design, expert assessment, model failure analysis, robustness standards, and building continuous evaluation systems that inform real-time investment decisions. They lead cross-cutting research initiatives to advance data perception, retrieval, post-training, and runtime orchestration, ensuring these advancements compound across the platform. The researcher influences platform-level decisions such as model lifecycle management, portfolio strategy, release criteria, and integration architecture to support human and agentic system co-evolution. Additionally, they mentor senior researchers to enhance experimental rigor and technical judgment, participate in hiring, and maintain hands-on research impact through technical work, publications, patents, and visible output.

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The Staff Applied AI Researcher at Articul8 AI is responsible for setting the technical direction for agentic reasoning systems and runtime intelligence across ModelMesh™, including defining orchestration strategies, decision policies, verification approaches, and runtime quality standards for parallel agent systems in production. They must architect infrastructure for large-scale researcher augmentation, design agentic platforms and orchestration primitives to enable extensive deployment of AI agents in experimentation, evaluation, and production integration. The role requires advancing the science of autonomous reasoning by designing, training, and refining learned components for runtime decisioning through widespread agent-driven experiment pipelines. The researcher must unify perception, retrieval, reasoning, and action by developing methodologies for integrating domain-specific models, data perception systems, knowledge graphs, retrieval layers, and external tools into cohesive agentic workflows. They lead research on agent reliability in regulated environments by driving failure detection, verification workflows, error analysis, and auditable autonomous behavior research using agent-orchestrated stress testing and red-teaming. The position entails defining evaluation methodologies for runtime intelligence to measure task success, decision quality, robustness, traceability, and failure recovery under enterprise conditions, building continuous agentic evaluation harnesses. Additionally, the researcher influences platform architecture decisions related to model routing, tool use, observability, governance, access control, and interoperability with external agent ecosystems. Mentoring researchers in the agentic paradigm, contributing to hiring, and maintaining a hands-on personal research impact through technical work, publications, patents, and visible output are also core responsibilities.