You'll work on well-scoped empirical open-ended ML research problems.

Responsibilities

• Train image classifiers and generative image models from scratch, and fine-tune open-weight language models.
• Get the most out of limited data, compute, and model-size budgets.
• Make models robust — to adversarial inputs and to adversarial conversations.
• Compress models to meet hard size and latency constraints without sacrificing accuracy.
• Diagnose and resolve training issues.

Requirements

We are looking for candidates with strong expertise in one or more of the following areas:

Adversarial Robustness.

Experience with:

• Adversarial training of image classifiers (e.g. PGD-based training, TRADES).
• Evaluating robust accuracy under standard threat models (e.g. L∞ attacks, AutoAttack) and avoiding gradient-masking pitfalls.
• Managing the robustness–accuracy trade-off and robust overfitting.

Efficient Computer Vision.

Experience with:

• Training image classifiers end-to-end, especially for fine-grained recognition (many visually similar classes, few examples per class).
• Model compression: quantization, pruning, and knowledge distillation from large teachers into small students.
• Deploying models under hard size or latency budgets (on-device, edge, or embedded settings).

Generative Image Modeling.

Experience with:

• Training image generative models from scratch: diffusion models, GANs, VAEs, or flow-based models.
• Iterating against sample-quality metrics such as FID.
• Training-efficiency tricks that produce good generators quickly and at small parameter counts.

LLM Post-Training & Behavioral Robustness.

Hands-on experience with one or more of:

• Supervised fine-tuning and preference optimisation (DPO, RLHF, RLAIF) of open-weight language models, including building your own datasets via synthetic generation, noisy or weak supervision, and rejection sampling.
• Shaping conversational behaviour over multiple turns: resistance to persuasion and sycophancy, calibrated confidence, and knowing when to accept corrections.
• Alignment-style fine-tuning that changes a specific behaviour while preserving general capability.

Multilingual Pre-training.

Experience with:

• Training multilingual or low-resource-language models from scratch.
• Tokenizer design across scripts and typologically diverse languages.
• Balancing highly unequal per-language data (sampling temperatures, cross-lingual transfer) in data-constrained regimes.

Additional Areas of Interest.

Experience in any of the following is a plus:

• Scaling laws and training-efficiency research.
• Curriculum learning and data ordering.
• Model evaluation: benchmark construction, contamination control, statistically sound comparisons.
• Uncertainty estimation and model calibration.
• Data augmentation and synthetic data for robustness.

General Qualifications.

• 3+ years of machine learning research experience (PhD research counts toward this requirement).
• Strong experience with PyTorch, JAX, TensorFlow, or similar ML frameworks.
• Degree from a top-100 university, experience at a FAANG or comparable AI company, or an equivalent research track record through publications or impactful open-source contributions.

Why Join

• Work on cutting-edge machine learning research.
• Collaborate with leading AI researchers on challenging, high-impact projects.
• Flexible, project-based work with competitive compensation.

Contract and Payment Terms

• You will be engaged as an independent contractor.
• This is a fully remote role that can be completed on your own schedule.
• Projects can be extended, shortened, or concluded early depending on needs and performance.
• Your work at Mercor will not involve access to confidential or proprietary information from any employer, client, or institution.
• Payments are weekly on Stripe or Wise based on services rendered.
• Please note: We are unable to support H1-B or STEM OPT candidates at this time.

Work arrangement: Fully remote

Eligible countries: United States