Staff/Principal Research Scientist

Inworld AI

. Conduct research on foundation models, evaluation, and frontier AI topics .

Posted 10/14/2026full-timeRemote • United KingdomLead💰 £140,000 - £200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in foundation models, reinforcement learning, and evaluation methodologies, with a strong emphasis on research publication and practical application in AI projects. Capable of full-stack research ownership, from experimental design to shipping results in production environments.

Highest-signal resume keywords

Foundation ModelsReinforcement LearningEvaluation MethodologiesPublished ResearchPhD in ML/NLP

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model TrainingNew ArchitecturesReward ModelingQuality MeasurementBenchmark DesignMultimodal ModelsExperiment DesignFailure AnalysisLarge-Scale Serving InfrastructureRealtime Inference

Soft Skills

Research FramingInterdisciplinary Collaboration

Tools & Technologies

LLM ServingModular APIsPublic AI Projects

Industry Keywords

ICMLICLRNeurIPSEMNLPACLAAAI

About the role

Key responsibilities & impact

Conduct research on foundation models, evaluation, and frontier AI topics
Train models and develop new architectures, reinforcement learning, reward modeling, and scaling approaches
Design benchmarks, evaluation loops, quality measurements, LLM-as-judge systems, and failure analyses
Research multimodal models, agents, tool use, test-time compute, and world models
Frame research questions and design experiments to investigate them
Write research papers and publish findings
Ship research results into production
Build and demonstrate public AI projects, interdisciplinary experiments, or open-source contributions
Work on first-party speech models, LLM serving, realtime inference, large-scale serving infrastructure, and modular APIs for high-volume realtime workloads

Requirements

What you’ll need

Experience with foundation models, including training, new architectures, reinforcement learning, reward modeling, and scaling
Experience with evaluation, including benchmarks, evaluation loops, quality measurement, LLM-as-judge, and failure analysis
Experience with frontier topics such as multimodal models, agents, tool use, test-time compute, and world models
Published research at ICML, ICLR, NeurIPS, EMNLP, ACL, or AAAI
PhD in ML/NLP or equivalent practical experience
Public work such as non-trivial AI side projects, interdisciplinary experiments, or open-source contributions
Full-stack research ownership, from framing questions and running experiments to writing papers and shipping results
Candidates must already have the legal right to work in the United Kingdom
Visa sponsorship is not available for this role

Benefits

Comp & perks

Equity
Benefits
Full U.S. visa and relocation support may be available for candidates relocating to the San Francisco Bay Area in the future, subject to business needs and applicable legal and work authorization requirements