Researcher, Context - Agent Post-Training
As a Context Researcher on the Agent Post-Training team, the role involves designing and running experiments to improve the scaling of compute on context. The researcher will own end-to-end improvements to the post-training stack, including reinforcement learning, data pipelines, graders, reward signals, evaluations, diagnostics, and model-behavior analysis. Responsibilities include building evaluations and environments to identify model failures and turning those failures into training data, product fixes, or new research directions. The researcher will partner with Codex and ChatGPT product teams to translate product signals into model improvements and work on early-training and alignment interventions such as data mixtures, objectives, synthetic data, and evaluation loops to shape downstream agent behavior. The role involves deciding which integrations, capabilities, and fixes are ready for major model runs, improving machinery for large-scale training and launch including experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. The researcher will take on cross-functional projects involving model training, product infrastructure, and the production agent harness and debug failures in shipped or near-shipped models by developing hypotheses, experiments, and fixes from qualitative behaviors.
Researcher, Connectors - Agent Post-Training
As a member of Agent Post-Training, Connectors, you will teach models how to interface with professional software using code, helping train agents to use code, APIs, tools, and structured integrations to operate across applications like Slack, Google Workspace, GitHub, Notion, Linear, Salesforce, and other core systems. You will design and run experiments to improve agentic model behavior for complex software and plugins, own end-to-end improvements to the post-training stack including RL, data pipelines, graders, reward signals, evaluations, diagnostics, and model behavior analysis, and build evaluations and environments that expose model failures to turn those failures into training data, product fixes, or new research directions. You will partner with product teams to understand user needs and translate product signals into model improvements, work on early-training and alignment interventions such as data mixtures, objectives, synthetic data, and evaluation loops, and decide which integrations and capabilities to include in major model runs. Additionally, you will improve large-scale training and launch infrastructure for experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness, take on cross-functional projects touching model training, product infrastructure, and the production agent harness, and debug failures in shipped or near-shipped models to develop concrete hypotheses, experiments, and fixes.
Researcher, Computer Use - Agent Post-Training
As a member of Agent Post-Training, Computer Use, you will teach models to operate computers, helping to train models that can navigate browsers and desktops, use tools and applications, reason through complex workflows, collaborate with users and other agents, and complete long-horizon tasks with reliability and judgment. Responsibilities include designing and running experiments to improve agentic model behavior for complex computer use, owning end-to-end improvements to the post-training stack such as reinforcement learning, data pipelines, graders, reward signals, evaluations, diagnostics, and model-behavior analysis. You will build evaluations and environments to identify model failures and convert those into training data, product fixes, or research directions. The role involves partnering with product teams to understand user needs and translate product signals into model improvements, working on early-training and alignment interventions, deciding on suitable integrations and fixes for major model runs, and improving large-scale training and launch machinery regarding experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. You will also handle cross-functional projects involving model training, product infrastructure, and production agent harness, debug failures in shipped or near-shipped models, and transform qualitative model behavior into concrete hypotheses, experiments, and fixes.
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.
Psychometrician (Internship)
As a psychometrician intern, you will help design, validate, and improve AI-powered assessments, working at the crossover between psychometrics and machine learning. Your day-to-day duties include building and validating psychometric assessments designed for AI integration, running analyses on item quality, fairness, reliability, and scoring accuracy, helping develop and refine automated scoring algorithms, exploring how large language models (LLMs) can be used to generate and evaluate assessment content, translating findings into clear insights for clients and internal teams, and contributing to research reports and potentially academic publications.
Senior Consultant - AI Training & Evaluation (MBB & Top-Tier Firms)
Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics including industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work such as market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, and benchmarking. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This role is remote, project-based, and focused on analytical design and evaluation as an individual contributor.
Senior Consultant - AI Training & Evaluation (MBB & Top-Tier Firms)
Build realistic consulting project environments including detailed project scenarios grounded in real engagement dynamics such as industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work including market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This role is remote, project-based, and focused on analytical design and evaluation as an individual contributor.
Senior Consultant - AI Training & Evaluation (MBB & Top-Tier Firms)
Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics such as industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work including market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task, which are used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.
Senior Consultant - AI Training & Evaluation (MBB & Top-Tier Firms)
Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics, such as industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents that mirror real consulting work, including market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, and benchmarking. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This role is remote, project-based, and individual-contributor focused on analytical design and evaluation.
Senior Consultant - AI Training & Evaluation (MBB & Top-Tier Firms)
Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics including industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work such as market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task, which are used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.
Access all 4,256 remote & onsite AI jobs.
Frequently Asked Questions
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.