ML Engineer

About The Job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark
, General Catalyst
, Peter Thiel
, Adam D'Angelo
, Larry Summers
, and Jack Dorsey
.

Machine Learning Engineer

Contract

$35/hour

India

30–40 hours/week

• Frame unique ML problems to enhance the capabilities of LLMs.
• Design, build, and optimize machine learning models for classification, prediction, NLP, recommendation, or generative tasks.
• Run rapid experimentation cycles, evaluate model performance, and iterate continuously.
• Conduct advanced feature engineering and data preprocessing.
• Implement adversarial testing, model robustness checks, and bias evaluations.
• Fine‑tune, evaluate, and deploy transformer‑based models where necessary.
• Maintain clear documentation of datasets, experiments, and model decisions.
• Stay updated on the latest ML research, tools, and techniques to push modeling capabilities forward.

• At least 2 years of full‑time experience in machine learning model development.
• Technical degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field.
• Demonstrated competitive machine learning experience (Kaggle, Driven Data, or equivalent).
• Evidence of top‑tier performance in ML competitions (Kaggle medals, finalist placements, leader board rankings).
• Strong proficiency in Python, PyTorch/Tensor Flow, and modern ML/NLP frameworks.
• Solid understanding of ML fundamentals: statistics, optimization, model evaluation, architectures.
• Experience with distributed training, ML pipelines, and experiment tracking.
• Strong problem‑solving skills and algorithmic thinking.
• Experience working with cloud environments (AWS/GCP/Azure).
• Exceptional analytical, communication, and interpersonal skills.
• Ability to clearly explain modeling decisions, tradeoffs, and evaluation results.
• Fluency in English.

• Kaggle Grand master, Master, or multiple Gold Medals.
• Experience creating benchmarks, evaluations, or ML challenge problems.
• Background in generative models, LLMs, or multimodal learning.
• Experience with large‑scale distributed training.
• Prior experience in AI research, ML platforms, or infrastructure teams.
• Contributions to technical blogs, open‑source projects, or research publications.
• Prior mentorship or technical leadership experience.
• Published research papers (conference or journal).
• Experience with LLM fine‑tuning, vector databases, or generative AI workflows.
• Familiarity with MLOps tools:
  Weights & Biases, MLflow, Airflow, Docker, etc.
• Experience optimizing inference performance and deploying models at scale.

• Upload resume
• AI interview based on your resume
• Submit form

• For details about the interview process and platform information, please check:
• For any help or support, reach out to:

PS:
Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.