AI Engineer - Model Post Training

We are seeking a skilled AI Engineer with hands-on experience post-training large language models at scale. This is a part-time, fully remote engagement for a practitioner who has done the work, not just studied it. You'll join a focused team working toward fine-tuning and aligning a 70B-parameter model, and your prior experience at that scale (or close to it) is essential.

Eligibility requirement

Applicants must be US citizens currently residing in the United States. We are unable to consider applicants based outside the US or those without US citizenship, regardless of work authorization status.

What you'll do

• Lead and contribute to post-training workflows including supervised fine-tuning, instruction tuning, DPO, RLHF, RLAIF, and related alignment techniques
• Apply QLoRA and other efficient fine-tuning methods to models across the 7B to 70B+ parameter range
• Train models for reliable structured output generation under adversarial input conditions
• Build and operate evaluation pipelines for safety-critical model behavior, including adversarial test suites, red-team integration (e.g., Garak), and regression tracking across model versions
• Calibrate decision thresholds against tiered policy configurations, including logprob-based confidence calibration at the serving layer
• Design training approaches that preserve inference-time policy specification, so model behavior can be adjusted without retraining
• Curate and prepare training data, evaluation sets, and preference data pipelines
• Iterate on training strategy to improve task performance, calibration, and adversarial robustness
• Document approach and decisions clearly for an async-first team

What we're looking for

• US citizen currently residing in the United States. This is a firm requirement.
• Concrete, verifiable production experience post-training open-weight LLMs. Experience at 7-8B, 13-30B, or 30B+ scales is all welcome, with larger-scale