Member of Technical Staff

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.