Research Fellow
FirstPrinciples Holding Company
full-time
Posted on:
Location Type: Remote
Location: Canada
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About the role
- Improve Theo’s ability to produce scientifically sound, high-quality outputs in 2026;
- Introduce new ideas, methods, and approaches that meaningfully shift system performance;
- Bring deep domain expertise (PhD+ level) in one or more targeted research areas.
- Research, design, and test novel model architectures that combine academic literature, NLP, symbolic reasoning, and structured scientific workflows.
- Prototype and build embedding representations for physical concepts, mathematical objects, and logical structures, enabling models to reason over equations, abstractions, and scientific constraints rather than surface text alone.
- Investigate alternatives to transformer-based architectures and deliver concrete recommendations.
- Design and run targeted experiments to evaluate new architectural ideas, using empirical results to guide the development of next-generation model architectures.
- Develop reinforcement learning loops that enable models to run internal and independent thought experiments.
- Design and automate scalable data ingestion pipelines that aggregate scientific literature, metadata, equations, and experimental data.
- Create custom benchmarks to measure physical understanding, mathematical reasoning, and failure modes in scientific reasoning and abstraction.
- Refine and release curated datasets and baselines once internal validation is complete.
- Run and track model training jobs while managing compute usage and budget constraints.
- Design sandbox environments for controlled autonomous exploration.
- Build evaluation frameworks using visual and statistical tools to identify strengths and blind spots.
- Implement tests and guardrails that flag low-quality or unsafe outputs.
- Maintain internal issue tracking with clear failure modes and fixes.
- Work closely with engineers to ensure research is feasible and production-ready.
- Communicate technical trade-offs clearly to non-technical stakeholders.
- Present regular research updates tied to defined milestones.
Requirements
- PhD or postdoctoral researchers in Computer Science, Machine Learning, Theoretical Physics, or a closely related field.
- Track record of research in either:
- model architectures, representation learning, or reasoning systems (CS/ML path); or
- mathematical, physical, or formal reasoning applied to fundamental problems (physics path).
- Demonstrated ability to translate abstract theory into testable computational systems.
- Comfortable working across disciplinary boundaries.
Benefits
- Fixed stipend for the full term
- Fully remote
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
NLPsymbolic reasoningreinforcement learningmodel architecturesrepresentation learningdata ingestion pipelinesempirical evaluationbenchmark creationmodel trainingautomated testing
Soft skills
communicationcollaborationproblem-solvingcritical thinkingresearch presentationinterdisciplinary worktechnical trade-off analysisproject managementadaptabilitycreativity
Certifications
PhDpostdoctoral research