Member of Technical Staff, AI Researcher

Red Cell PartnersArtificial intelligence

Location: Remote (USA)Full-TimeStaff

Salary$250,000-400,000 + equity

Apply NowOpens the employer's application page

Job Details

Have 5+ years of relevant experience in machine learning, artificial intelligence, computer science, data science, software engineering, or a related technical field.
Demonstrate the ability to work at the intersection of AI research and software engineering.
Have strong Python skills and experience building reliable, scalable software.
Have hands-on experience with AI agents, model evaluations, reinforcement learning, post-training, or related systems.
Understand frontier AI methods, including benchmarks, evaluation techniques, agent harnesses, post-training approaches, and training-data design.
Have experience building or working with reward models, automated verifiers, graders, or other model-feedback systems.
Have experience designing evaluations or experiments that measure model capability, generalization, alignment, or agent performance.
Be able to build research prototypes and develop them into maintainable, production-quality systems.
Have analytical, problem-solving, written communication, and technical documentation skills.
Be a U.S. citizen.
An advanced degree in machine learning, artificial intelligence, computer science, data science, or a related field is preferred but not required.

Design evaluation frameworks, benchmarks, and methodologies for frontier large language models, AI agents, multimodal models, and reinforcement learning environments.
Develop reward models, programmatic verifiers, graders, and other feedback systems to measure and improve model behavior.
Design and run supervised fine-tuning and reinforcement learning experiments to measure dataset effects on model capability, generalization, and alignment.
Research and define evaluations that represent real-world work and operational environments.
Build systems for producing high-quality human data, including expert task design, annotation methodologies, data-quality signals, and data-attribution techniques.
Translate research questions into lightweight, rigorous experiments and interpret results to guide technical decisions.
Prototype, evaluate results, diagnose failure modes, and improve benchmarks, datasets, or technical systems.
Move promising methods from experimentation into scalable, production-quality implementations.
Work across research, engineering, product, and operations on a shared technical roadmap.
Contribute benchmarks, open-source tools, research, and technical writing to the broader technical community.

View Full Description & ApplyYou'll be redirected to the employer's site