Simular logo

PhD Research Intern

Simular

SingaporeHybridFull-timeNo compensation foundPosted 11mo agoChecked 2w ago
Evaluate my fit

Most applications go out cold — see where you stand first. No sign-up to start.

Don't just apply. Show up ready.

Olive works from this exact posting.

Evaluate your fitSee where you stand — strengths, gaps, and what to fix first.Get started →Tailor your résuméRewritten against this posting, with a diff of every change.Get started →Practice the interviewA voice mock interview built from this job description.Get started →

At a glance

Compensation
No compensation found
Location
SingaporeHybrid
Role Type
Internship
Schedule
Full-time
Work Authorization
Not specified

Requirements

Credentials this posting asks for.

Doctorate

Job overview

Simular is hiring a PhD Research Intern. Simular is seeking a PhD Research Intern to collaborate with research scientists on advancing methods in planning and reinforcement learning for computer use, multimodal grounding, reward/judge modeling, and user intent understanding. The intern will contribute to building datasets, running experiments, and benchmarking results, while exploring novel approaches to derisk Simular's long-term technical roadmap. They will also document and communicate findings through internal reports or academic-style writing.

Key focus areas include Collaborate with research scientists to advance methods, Contribute to building datasets, running experiments, and benchmarking results, and Explore novel approaches and help derisk Simular’s long-term technical roadmap.

Successful candidates bring PhD In Computer Science Or Machine Learning, Research Background In Reinforcement Learning Or Large Language Models, and Experience Publishing In Top‑Tier Conferences. Important skills include Reinforcement Learning, Large Language/Vision-Language Models, Computer Vision And Multimodal Perception, Representation Learning, Strong Coding Skills, and Prototyping Skills. Preferred (not required): Planning And RL For Computer Use, Behavioral Cloning, RL On Model Weights, and RAG-based Domain Knowledge.

Skills & qualifications

RequiredNice to have

Skills

Reinforcement LearningLarge Language/Vision-Language ModelsComputer Vision and Multimodal PerceptionRepresentation LearningStrong Coding SkillsPrototyping SkillsPythonPyTorchJAXPlanning and RL for Computer UseBehavioral CloningRL on Model WeightsRAG-Based Domain KnowledgeMultimodal GroundingVision-Only ModelsTree SearchHybrid Methods With Large ModelsReward/Judge ModelingError AnalysisHuman EvaluationTraining Judge ModelsUser Intent UnderstandingModeling Vague QueriesPreference LearningBuilding DatasetsRunning ExperimentsBenchmarking ResultsExploring Novel ApproachesDocumenting FindingsCommunicating FindingsAcademic-Style WritingCuriosityInitiativeBridging Fundamental Research With Applied AI

Qualifications

PhD in Computer SciencePhD in Machine LearningPhD in Related FieldExperience Conducting ExperimentsExperience Publishing or Preparing Papers in Top-Tier Conferences

Full job description

Where multiple locations are listed for this role, the position may be based in any of those locations, with priority determined according to the order of listing.

What you’ll do

As a PhD intern, you will:

  • Collaborate with research scientists to advance methods in:

  • Planning and RL for computer use (e.g. behavioral cloning, RL on model weights, RAG-based domain knowledge)

  • Multimodal grounding (e.g. vision-only models, tree search, hybrid methods with large models)

  • Reward/judge modeling (e.g. error analysis, human evaluation, training judge models)

  • User intent understanding (e.g. modeling vague queries, preference learning)

  • Contribute to building datasets, running experiments, and benchmarking results

  • Explore novel approaches and help derisk Simular’s long-term technical roadmap

  • Document and communicate findings through internal reports or academic-style writing

You might be a fit if

  • Currently pursuing a PhD in Computer Science, Machine Learning, or related field

  • Research background in at least one of: Reinforcement learning, Large language/vision-language models, Computer vision and multimodal perception, Representation learning

  • Experience conducting experiments and publishing or preparing papers in top-tier conferences (NeurIPS, ICLR, ICML, CVPR, ACL, etc.)

  • Strong coding and prototyping skills in Python and ML frameworks (PyTorch/JAX)

  • Curiosity, initiative, and interest in bridging fundamental research with applied AI

You've read the whole posting — now see how you match it.

Evaluate my fit