Website:
basethesis.com
Job details:
BaseThesis Labs, A frontier lab focused on democratising the way humans interact with technology to maximise their potential.
Hiring researchers and engineers to build with us.
What BaseThesis expects from you?
- High ownership on your research direction. You drive the research objective / experiments / conclusive derivations of your work.
- Measurable results or clean negative findings. Clarity over everything else.
- Exceptional engineering skills and managing production grade systems at scale.
- BaseThesis affiliation on preprints, arXiv submissions, and conference papers
- Open source release of models, code, and datasets on BaseThesis's GitHub and Hugging Face, unless explicitly scoped otherwise
- Willingness to ship, as a paper, as a prototype that graduates to a product team, or as infrastructure other researchers use
- What BaseThesis provides you:
- Dedicated compute for your program = no GPU rationing, no shared queue waits.
- A principal investigator on a 24 month horizon, resourced to produce one finding the field did not believe was possible.
- Primary authorship on your research papers, with editorial feedback on drafts.
- Reimbursements (travel, stay, attendance) to present your work at top venues = main tracks only at NeurIPS, ICML, ICLR, CoRL, RLC.
- Access to our internal community of founders, researchers, systems engineers, and product builders working across the lab.
- Unconventional Applied Research; Seeing your research deployed inside real businesses through BaseThesis's portfolio companies, not stopping at the preprint.
- Co-authorship on cross-program collaborations, proportional to contribution.
Distribution of your work through BaseThesis's channels when it ships.
Roles:
Applied AI Engineer / Systems Engineer / Full-Stack Engineer
- Ship, maintain and improve production systems end-to-end, owning the path from research prototype to deployed infrastructure.
- Distributed training or real-time inference at scale. Eg., multi-node orchestration, low-latency serving, compiler-level optimization where needed.
- Fluent across the entire stack from model code, data pipelines, APIs, to frontend when the system needs one. The boundary between "ML" and "engineering" should feel arbitrary to you.
- Multi-cloud orchestration, petabyte-scale data pipelines, or custom silicon integration, depth in at least one, literacy in the rest.
AI Researcher
- First-author track record at top venues (NeurIPS, ICML, ICLR, CVPR, ACL, or equivalent), or clear evidence you're producing work at that level.
- Depth in at least one of World Models, post-transformer architectures, neural interfaces, continual learning, pure RL or swarm intelligence.
- Strong mathematical foundation: probability, linear algebra, optimization.
Click on Apply to know more.