Senior ML Engineer | Up to $21/hr

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This range is provided by Mercor. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$21.00/hr - $21.00/hr

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 .

Position: Machine Learning Engineer

Type: Contract

Compensation: $21/hour

Location: Remote

Commitment: 30–40 hours/week

Role Responsibilities

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.

Qualifications

Must-Have

At least 3–5 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, DrivenData, or equivalent).
Evidence of top‑tier performance in ML competitions (Kaggle medals, finalist placements, leaderboard rankings).
Strong proficiency in Python, PyTorch/TensorFlow, 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.

Preferred

Kaggle Grandmaster, 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.