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Remote role with attractive frontier-AI work creates moderate applicant density despite niche ML research specialization.
Deep ML research and simulation expertise required, limiting cross-industry transferability.
Requires strong research pedigree, publications, RL/simulation expertise, and post-training experience, making filters stringent.
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Develop and maintain ultra-realistic, long-horizon simulation environments (SimLabs) to test and improve advanced AI agents.
Create programmatic, policy-aware evaluation systems to rigorously measure AI capability and safety beyond standard benchmarks.
Design and execute high-quality post-training runs (CPT, SFT, RL) using curated data to enhance open-source model performance, iterating rapidly across the ML stack.
Bachelor's, Master's, or PhD in a technical field (CS, Math, Physics, etc.) or equivalent demonstrated work through open-source or industry experience.
Strong software engineering skills with experience building robust, scalable infrastructure; proficiency in Python and CLI development environment.
Principled understanding of foundation models including construction, evaluation, and optimization.
Work Experience Required: Not explicitly mentioned in the JD.
Has prior experience or domain expertise in Reinforcement Learning, simulation systems, or building long-horizon agentic environments.
Demonstrates a track record of impactful ML research (publications at top conferences like NeurIPS, ICLR, ICML) or maintaining influential open-source projects.
Operates with an impact-driven, proactive mindset, identifying system gaps and driving solutions that improve real-world AI model metrics.