Human Baseliner for Open-Ended ML Research Tasks

Overview

We are hiring experienced machine learning engineers and researchers to serve as human baseliners 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.

What You’ll Do

• 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

Commitment

• Minimum 20 hours per week if selected

• More availability is strongly preferred

Requirements

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 TensorFlow

• 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

Required Domain Expertise

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

Logistics (work trial requirements)

• 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