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AI Research Engineer
Summary by AIEngineer.careers
AI Research Engineer is a AI Research Engineer role (full-time). with TTN Talent. in SAN FRANCISCO, US. Compensation shown: $200K–$400K. Imported listing (source: jobalert.world). Apply on the employer's site (jobalert.world).
Imported listing (source: jobalert.world) · Apply on jobalert.world
SAN FRANCISCO · US
$200K–$400K
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from jobalert.world
Join an early-stage AI infrastructure company to build high-impact benchmarks, datasets, and environments that improve frontier model reasoning and agent performance. This role focuses on post-training methods like RLHF and DPO, requiring strong hands-on coding skills alongside research capabilities. Ideal for candidates with experience in LLMs, agents, and AI safety who can operate autonomously on ambiguous technical problems.
Responsibilities
- Build practical AI/ML research projects, benchmarks, and evaluations for LLMs and agents
- Develop and implement post-training methods such as RLHF, DPO, and distillation
- Create high-impact datasets and environments to improve model reasoning and agent performance
- Conduct high-stakes model evaluation and contribute to AI safety and alignment research
Requirements
- Experience in AI research across LLMs, agents, evaluations, benchmarking, AI safety, or alignment
- Hands-on experience with post-training methods (RLHF, DPO, distillation)
- Strong hands-on coding skills; ability to build, test, and ship rather than just write papers
- Ability to operate autonomously on ambiguous technical problems
- Fresh PhD graduates are considered
TT
TTN Talent
United States · US · 2+ employees
Founded by Dan Kirkpatrick, TTN Talent (Through the Noise Talent) is a specialized recruitment firm focused on hiring for AI, machine learning, and frontier research roles. The company aims to provide a more thoughtful and precise approach to hiring by limiting the number of simultaneous assignments to ensure quality and depth in candidate qualification.
