[Contract] LLM Research Scientist (Pre-training & Computer Vision & Adversarial Robustness)

1000scholars; United States; A distancia; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Mercor is seeking an experienced LLM Research Scientist to advance pre-training, computer vision, and adversarial robustness. This fully remote role is open to candidates in the United States, with responsibilities spanning end-to-end model development and evaluation.

You will train vision-language models, compress models to meet latency constraints, address data and compute limits, and push robustness against adversarial inputs while improving sample efficiency.

Formación

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

Responsabilidades

• 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.

Conocimientos

3+ years ML research experience

Strong experience with PyTorch, JAX,TF

Educación

Degree from a top-100 university

Herramientas

PyTorch

JAX

TensorFlow

Descripción del empleo

[Contract] LLM Research Scientist (Pre-training & Computer Vision & Adversarial Robustness)

Mercor

Remote

We're looking for experienced machine learning researchers with hands-on experience training and improving deep learning models end‑to‑end, across vision and language. 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

• 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.

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

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.