Post-Training ML Scientist for Offensive Security

Oscar Faye; Palo Alto (CA); On-site; USD 180,000 - 320,000; Full time

Benefits offered by this job

Housing stipend

Relocation assistance

Job summary

Unknown is seeking a high-impact researcher to own the post-training lifecycle for an offensively oriented AI security platform. You will train, evaluate, and refine models that operate in live environments, translating expert tradecraft into scalable data signals.

You will work across research, platform engineering, and security practitioners to push the frontier of model capabilities after base training, with a focus on reliability, efficiency, and real-world applicability.

Qualifications

• PhD in CS or ML, or an equivalent track record.
• Hands-on depth in post-training: SFT, RLHF or RLAIF, DPO and preference optimization, reward modelling.
• Demonstrated ability to train and evaluate models in practice.
• Engineering ability to execute your own ideas at scale.
• Works across teams (research, platform engineering, practitioners).

Responsibilities

• Own the post-training pipeline end to end.
• Design and run evaluation benchmarks for offensive-security capability.
• Turn practitioner tradecraft into structured training data.
• Ensure model improvements translate to production usage against live environments.

Skills

Post-training

SFT

RLHF

RLAIF

Reward modelling

Evaluation in practice

Cross-team collaboration

Education

PhD in CS or ML

Job description

Unknown is seeking a high-impact researcher to own the post-training lifecycle for an offensively oriented AI security platform. You will train, evaluate, and refine models that operate in live environments, translating expert tradecraft into scalable data signals.

You will work across research, platform engineering, and security practitioners to push the frontier of model capabilities after base training, with a focus on reliability, efficiency, and real-world applicability.