Technical Staff - Search Guidance (Remote)

Location: Remote Type: Full-time

Description

Ndea is building AGI systems that depend on search guidance. We're hiring hands-on researchers/engineers to drive our deep learning effort in search guidance, training models to make structured search more efficient and reliable. This is a high-ownership role for someone who wants to operate at the leading edge of neuro-symbolic AI, designing, implementing, and validating new approaches in code. We offer:

Meaningful equity, competitive salary, and benefits
Aggressive compute budget
Small, high-talent-density, globally remote team

Ndea is an equal opportunity employer and does not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.

How to Apply

If you're interested in this role, send us an email including the following items to: future@ndea.com

Your location (city, country)
Something impressive thing you've created or published, ideally in the reinforcement learning or search guidance space
Share any experience/training you have in symbolic systems, logic programming, formal methods, or discrete math
Links to your profile(s) (e.g., Google Scholar, GitHub, X, LinkedIn, etc.)
Your resume (optional if the above links are sufficient)

Referral Bonus

Know someone who might be a good fit for this role? Refer them to us, and if they're hired and stay for 30 days, you could earn a $10,000 bonus.

Qualifications

Strong hands-on experience building and debugging RL or search-driven learning systems
Research contributions in reinforcement learning, planning, or search (papers and/or industry work)
Evidence of turning research ideas into working implementations
Comfort operating as an individual contributor in a small, high-agency environment
Interest in symbolic approaches (program synthesis, logic, solvers, formal methods, etc.)
Clear communication and documentation skills for complex research/engineering workflows

Nice-to-have

Experience with search-guided learning in structured or combinatorial domains
Work in planning-heavy, exploration-heavy, or solver-adjacent settings
Familiarity with discrete mathematics, verification, or theorem-proving style problems

This role may not be a fit if you’re primarily focused on…

LLM-only RLHF or post-training pipelines
World model scaling or agent abstraction layers as the main artifact