Remote Human Baseliner ML Research Tasks - AI Trainer

Overview

We are hiring experienced machine learning engineers and researchers to serve as human base liners for evaluations of open-ended machine learning research tasks. These evaluations measure how well AI agents perform on realistic AI R&D problems. To interpret agent performance, we also need strong human reference points: skilled practitioners attempting the same tasks under the same time and compute constraints. As a baseliner, you will complete self-contained ML research tasks in a sandboxed environment, working independently with your preferred tools and workflow.

Your performance will be used as a benchmark against which frontier-model agents are evaluated.

• Attempt open-ended machine learning research tasks under a fixed time and compute budget (work trial)
• Work independently in a sandboxed Linux environment with internet access
• Use your preferred tooling, including IDEs and AI coding assistants such as Cursor, Claude Code, and ChatGPT
• Record your full working session via screen recording
• Complete a short pre-task and post-task questionnaire
• Submit your final work product, screen recording, and completed questionnaires. Post this you will be hired for a longer commitment

Minimum 20 hours per week if selected

More availability is strongly preferred

Candidates must meet all of the following:

• 3+ years of machine learning experience - Time spent in a PhD program counts toward this requirement
• Undergraduate and master’s experience does not count
• Attended a top-100 university or worked at FAANG or a comparable company
• Experience with at least one major ML framework such as PyTorch, JAX, or Tensor Flow
• Deep, hands‑on expertise in at least one of the focus areas below:
  • Pretraining under tight data and compute budgets
  • PPO, reward shaping, custom `gym` / `gymnasium` environments, and throughput tuning
  • Full fine‑tuning, LoRA, QLoRA, DPO, RLHF, RLAIF, and distillation
  • Large‑scale corpus filtering, deduplication, subsampling, and benchmark contamination avoidance
  • Architecture design under strict parameter‑count or size constraints
  • Modifying pretrained architectures, including attention patterns, pooling heads, or training objectives
  • Contrastive training for embedding or retrieval models
  • Generative vision or video modeling
  • Multilingual or low‑resource language experience
  • Image or video data pipelines at scale
  • Experience balancing competing model objectives such as safety and capability
  • Prior work as an ML evaluator, red‑teamer, or baseliner

Candidates must have strong practical experience in at least one of the following:

• Pretraining
  : training transformer language models from scratch
• Reinforcement learning
  : training agents in custom or existing environments
• Post‑training
  : fine‑tuning and aligning LLMs
• Dataset curation
  : building and cleaning large text corpora for LLM training
• Model architecture
  : designing and modifying neural network architectures

• One baseline attempt per contractor per task
• Each task may only be attempted once by a given contractor
• All work is confidential and covered by NDA
• Compute and environment are provided; no personal GPU is required