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Job summary
A leading tech company is seeking a Machine Learning Engineer to design scalable ML pipelines and build deep learning models. The ideal candidate will have a strong background in machine learning and be proficient in Python, using frameworks like PyTorch and TensorFlow. This is a remote, hourly contractor position with weekly pay through Stripe Connect. Applicants will undergo an AI interview and must complete a resume upload as part of the application process.
Qualifications
- Strong background in machine learning, deep learning, or reinforcement learning.
- Proficient in Python and familiar with frameworks such as PyTorch, TensorFlow, or JAX.
- Understanding of training infrastructure including distributed training, GPUs/TPUs, and data pipeline optimization.
- Experience with MLOps tools like Weights & Biases, MLflow, Docker, Kubernetes, or Airflow.
- Experience designing custom architectures or adapting LLMs, diffusion models, or transformer-based systems.
Responsibilities
- Design and implement scalable ML pipelines for model training, evaluation, and continuous improvement.
- Build and fine-tune deep learning models for reasoning, code generation, and real-world decision-making.
- Collaborate with data scientists to collect and preprocess training data, ensuring quality and representativeness.
- Develop benchmarking tools that test models across reasoning, accuracy, and speed dimensions.
- Implement reinforcement learning loops and self-improvement mechanisms for agent training.
- Work with systems engineers to optimize inference speed, memory efficiency, and hardware utilization.
Skills
Tools
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: Hourly contractor
Compensation: $14/hour
Location: Remote
Commitment: 20–40 hours/week
- Design and implement scalable ML pipelines for model training, evaluation, and continuous improvement.
- Build and fine-tune deep learning models for reasoning, code generation, and real-world decision-making.
- Collaborate with data scientists to collect and preprocess training data, ensuring quality and representativeness.
- Develop benchmarking tools that test models across reasoning, accuracy, and speed dimensions.
- Implement reinforcement learning loops and self-improvement mechanisms for agent training.
- Work with systems engineers to optimize inference speed, memory efficiency, and hardware utilization.
- Strong background in machine learning, deep learning, or reinforcement learning.
- Proficient in Python and familiar with frameworks such as PyTorch, TensorFlow, or JAX.
- Understanding of training infrastructure, including distributed training, GPUs/TPUs, and data pipeline optimization.
- Experience with MLOps tools (e.g., Weights & Biases, MLflow, Docker, Kubernetes, or Airflow).
- Experience designing custom architectures or adapting LLMs, diffusion models, or transformer-based systems.
- Hourly contractor
- Paid weekly via Stripe Connect
- Upload resume
- AI interview based on your resume
- Submit form
- For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome/welcome
- For any help or support, reach out to: support@mercor.com
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.