AI Engineer - Model Post Training

Wider Security LLC is hiring a part-time
, fully remote AI Engineer to focus on post-training and alignment for large language models  work centers on supervised fine-tuning and safety-focused evaluation, with hands‑on experience needed to support model tuning from 7B through 70B and beyond
.

This role is built for an async-first team and emphasizes measurable outcomes across training, calibration, and adversarial robustness. You will help lead end-to-end post-training workflows, from data preparation through evaluation and deployment-oriented integration.

• Lead and contribute to post-training workflows including supervised fine-tuning
  , instruction tuning,
  DPO
  , RLHF
  , RLAIF
  , and related alignment techniques
• Use QLoRA and other efficient fine-tuning methods across models spanning 7B to 70B+ parameter ranges
• Train models to produce reliable structured outputs under adversarial input conditions
• Build and run evaluation pipelines for safety-critical behavior, including adversarial test suites, red-team integration (for example,
  Garak
  ), and regression tracking across model versions
• Calibrate decision thresholds to support tiered policy configurations, including logprob-based confidence calibration at the serving layer
• Design training approaches that preserve inference-time policy specification
  , enabling behavior changes 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 approaches and decisions clearly for a distributed, async-first team

• US citizenship and current residency in the United States (firm requirement)
• Concrete, verifiable production experience post-training open-weight LLMs, with experience at 7-8B
  , 13-30B
  , or 30B+ scales welcomed, and larger-scale experience a plus
• Experience with modern open-weight model families such as Llama
  , Qwen
  , Mistral
  , or similar
• Hands‑on experience with QLoRA
  , LoRA
  , and efficient fine-tuning methods for large models
• Background in alignment approaches including SFT
  , DPO
  , RLHF
  , RLAIF
  , or constitutional AI approaches
• Experience training models for reliable structured output (JSON, schema-constrained generation, function-call style outputs)
• Familiarity with serving stacks such as vLLM
  , TGI
  , or similar, plus comfort working with logprob-level model outputs
• Deep familiarity with distributed training frameworks including Deep Speed
  , FSDP
  , Megatron-LM
  , or similar
• Proficiency in Python and comfort with multi-GPU
  , multi-node training infrastructure
• Ability to work independently and manage time effectively in a part-time
  , async-first environment

• Python
  , QLoRA
  , LoRA
  , SFT
  , DPO
  , RLHF
  , RLAIF
• constitutional AI
  , vLLM
  , TGI
  , Deep Speed
  , FSDP
  , Megatron-LM
• Llama
  , Qwen
  , Mistral
  , Garak

Applicants must be US citizens currently residing in the United States
. The company is unable to consider applicants outside the US or without US citizenship, regardless of work authorization status.

• Direct experience training safety classifiers, content moderation models, jailbreak or prompt injection detectors, or other trust-and-safety ML systems
• Experience with adversarial evaluation frameworks such as Garak
  , promptfoo
  , or similar
• Comfort with deployment constraints typical of regulated or restricted-network environments

• Published research or open-source contributions related to LLM training, alignment, or AI safety
• Prior work at an AI lab, a foundation model team, or on a production safety classifier
• Experience designing or operating tiered policy systems where model behavior can be modulated at inference time

Location: Newport, RI (remote)
Job type: part time
Pay: USD 100 - 250 per hour