AI Applied Research Scientist Jobs

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Lead research that advances Maincode's work on capable, useful, and trustworthy AI systems. Design and execute experiments, develop new research directions, and collaborate closely with researchers and engineers. Produce research outputs suitable for top-tier conferences, journals, technical reports, open-source releases, or deployment in Matilda and future Maincode systems.

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As a member of the Agent Post-training Personality team, the role involves helping to make OpenAI’s agents exceptional collaborators by studying what makes an agent thoughtful, clear, perceptive, appropriately proactive, and easy to work with. This includes translating those insights into evaluations, training data, reward signals, and model improvements. Responsibilities include developing a rigorous understanding of effective agent collaboration across various types of work, turning qualitative judgments about model behavior into concrete hypotheses, evaluations, graders, and training interventions, studying user signals to understand behaviors that create trust and satisfaction, working with human experts and trainers to produce high-quality data capturing excellent collaborative behavior, improving reward models and reinforcement learning objectives, collaborating with pretraining and early-training teams on data and objectives, building pipelines for updating training data, partnering with product teams to turn consumer insights into model improvements, and owning projects end to end from identifying behavioral failures through experimentation, training, evaluation, and launch.

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Research Engineers design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems. They develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases. They investigate how different post-training techniques affect system behavior across enterprise workflows and production constraints. They build infrastructure for experimentation, model comparison, regression testing, and behavior analysis. Research Engineers partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems. They analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement. They create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability. They communicate clearly with internal teams and customer stakeholders about model behavior, evaluation results, limitations, and tradeoffs.

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As an Applied Research Engineer at Labelbox, you will create frameworks and tools to construct, train, benchmark, and evaluate autonomous agent capabilities. You will design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies. You will develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems. You will implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. You will engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices. You will collaborate closely with frontier AI lab customers to understand their requirements and guide model development. Additionally, you will publish research findings in academic journals, conferences, and blog posts.

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As an Applied Research Engineer at Labelbox, you are responsible for creating frameworks and tools to construct, train, benchmark, and evaluate autonomous agent capabilities. You design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies. You develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems. You implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. You engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs and share best practices. You collaborate closely with frontier AI lab customers to understand their requirements and guide model development. Additionally, you publish research findings in academic journals, conferences, and blog posts.

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The role involves improving the capabilities, reliability, and product fit of OpenAI’s agentic models for power users and API developers. Responsibilities include designing and running experiments to enhance model behavior in API and power-user workflows such as function calling, tool use, coding, planning, and long-horizon execution. The role requires building evals, graders, and environments from real developer and power-user workflows, turning observed failures into training data, hypotheses, and improvements. The researcher partners with API and power-users to identify behavior gaps and translate product signals into post-training interventions. They improve model behavior when composed into systems, ensuring reliable tool use, respect for developer intent, appropriate error handling, clarification when needed, and task coherence. The role also includes owning end-to-end model behavior projects from failure analysis through training, eval design, integration into major model runs, and launch readiness. Developing feedback loops using power-user traces and production-like environments to identify model failures and gaps is part of the job. The researcher assists in deciding which capabilities, fixes, and integrations are ready for major model runs. Additionally, debugging hard failures in models by analyzing traces, evals, training data, and product context is required. The role involves working on early-training and alignment interventions, improving large-scale training and launch machinery, and taking on cross-functional projects that touch model training, product infrastructure, and production agent harnesses, including multi-agent systems and training against production-like environments.

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In this role, you will push the frontier of visual generative models. You will work on large-scale pre-training for text-to-image foundation models, shaping objectives, algorithms, data, and systems, and turn novel ideas into models that power products used by millions of users. You will work with a creative and ambitious team of researchers and engineers building the future of the creative economy.

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The Data Understanding team is responsible for creating high quality datasets and their quantized representations for OpenAI, which includes synthesizing data, building VQ representations, processing, filtering, deduplication, quality control, and tokenization to enable effective use in large model training runs. The role involves advancing how OpenAI builds and understands pretraining data at scale by treating data quality and curation as core research problems. Responsibilities include developing new methods to select, combine, and transform data, creating datasets that improve model capabilities, designing rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior, and working closely with frontier models and web-scale data to build evidence for effective approaches and translate successful research into scalable data processing pipelines.

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The Data Understanding team is responsible for creating high quality datasets and their quantized representation for OpenAI, which includes synthesizing multimodal data, building VQ representations, processing, filtering, deduplication, quality control, and tokenization for effective use in big model training runs. The role involves advancing how OpenAI prepares, curates, synthesizes, and understands multimodal data at scale. Responsibilities include working on research and production problems such as synthesizing multimodal content (images, audio, and video) and their supervisions, improving noisy data pipelines, building better quality filters, using models to automate data preparation, and measuring whether changes in the dataset improve model performance. The position also requires owning and driving a research agenda, choosing the right multimodal data problems, and carrying long-running work through to impact, while engaging in an empirical, collaborative approach to research.

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The Senior Scientist is responsible for owning the end-to-end analytical strategy for GC-MS-based programs, including method design, validation frameworks, and data quality standards for targeted and untargeted analyses. They define and evolve sample preparation methodologies for headspace, liquid-phase, and solid-phase extraction of fragrance compounds from complex matrices and consumer products. They maintain and improve Osmo's high-throughput analytical pipeline, ensuring data integrity, reproducibility, and compatibility with downstream machine learning workflows. The role involves partnering with the Platform and ML teams as the chemistry-side technical owner of the data interface, determining methods and procedures for new analytical assignments independently while coordinating execution across team members and collaborating functions. They enforce high standards of scientific rigor and data quality, mentor and develop junior and mid-level scientists, establish best practices, review work for scientific integrity, and elevate the team’s overall analytical capability. Additional responsibilities include writing, editing, and auditing analytical and experimental protocols, serving as an internal expert resource and external-facing collaborator for analytical chemistry questions across Osmo’s scientific and commercial programs.