Deep Learning Engineer
About the role
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: Hourly contractor Compensation: $14/hour Location: Remote Commitment: 20–40 hours/week Role 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.
Qualifications Must-Have
- 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.
Compensation & Legal
- Hourly contractor
- Paid weekly via Stripe Connect
Application Process (Takes 20–30 mins to complete)
- Upload resume
- AI interview based on your resume
- Submit form
Resources & Support
- 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. ,
Deep Learning Engineer
About the role
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: Hourly contractor Compensation: $14/hour Location: Remote Commitment: 20–40 hours/week Role 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.
Qualifications Must-Have
- 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.
Compensation & Legal
- Hourly contractor
- Paid weekly via Stripe Connect
Application Process (Takes 20–30 mins to complete)
- Upload resume
- AI interview based on your resume
- Submit form
Resources & Support
- 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. ,