Machine Learning Engineer India

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 4 hours ago

Vacancies: 1 Vacancy

Job Summary

Mercor is hiring a Machine Learning Engineer to help design train and deploy large-scale learning systems powering autonomous AI agents for its AI lab partner. This role is ideal for engineers passionate about building models that think adapt and perform complex tasks in real-world environments. Youll be working at the intersection of ML research systems engineering and AI agent behavior transforming ideas into robust scalable learning pipelines.

Youre a great fit if you:

Have a strong background in machine learning deep learning or reinforcement learning.
Are proficient in Python and familiar with frameworks such as PyTorch TensorFlow or JAX.
Understand training infrastructure including distributed training GPUs/TPUs and data pipeline optimization.
Can implement end-to-end ML systems from preprocessing and feature extraction to training evaluation and deployment.
Are comfortable with MLOps tools (e.g. Weights & Biases MLflow Docker Kubernetes or Airflow).
Have experience designing custom architectures or adapting LLMs diffusion models or transformer-based systems.
Think critically about model performance generalization and bias and can measure results through data-driven experimentation.
Are curious about AI agents and how models can simulate human-like reasoning problem-solving and collaboration.

Primary Goal of This Role

To develop optimize and deploy machine learning systems that enhance agent performance learning efficiency and adaptability. Youll design model architectures training workflows and evaluation pipelines that push the frontier of autonomous intelligence and real-time reasoning.

What Youll Do

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.
Maintain model reproducibility and version control integrating with experiment tracking systems.
Contribute to cross-functional research efforts to improve learning strategies fine-tuning methods and generalization performance.

Why This Role Is Exciting

Build the core learning systems that power next-generation AI agents.
Combine ML research engineering and systems-level optimization in one role.
Work on uncharted challenges designing models that can reason plan and adapt autonomously.
Collaborate with a world-class AI team redefining how autonomous systems learn and evolve.

Pay & Work Structure

Youll be classified as an hourly contractor to Mercor.
Paid weekly via Stripe Connect based on hours logged.
Part-time (20 hrs- 40 hrs/week) with fully remote async flexibility work from anywhere on your own schedule.
Weekly Bonus of $500 - $1000 per 5 task created.

Mercor is hiring a Machine Learning Engineer to help design train and deploy large-scale learning systems powering autonomous AI agents for its AI lab partner. This role is ideal for engineers passionate about building models that think adapt and perform complex tasks in real-world environments. You...

Youre a great fit if you:

Have a strong background in machine learning deep learning or reinforcement learning.
Are proficient in Python and familiar with frameworks such as PyTorch TensorFlow or JAX.
Understand training infrastructure including distributed training GPUs/TPUs and data pipeline optimization.
Can implement end-to-end ML systems from preprocessing and feature extraction to training evaluation and deployment.
Are comfortable with MLOps tools (e.g. Weights & Biases MLflow Docker Kubernetes or Airflow).
Have experience designing custom architectures or adapting LLMs diffusion models or transformer-based systems.
Think critically about model performance generalization and bias and can measure results through data-driven experimentation.
Are curious about AI agents and how models can simulate human-like reasoning problem-solving and collaboration.

Primary Goal of This Role

What Youll Do

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.
Maintain model reproducibility and version control integrating with experiment tracking systems.
Contribute to cross-functional research efforts to improve learning strategies fine-tuning methods and generalization performance.

Why This Role Is Exciting

Build the core learning systems that power next-generation AI agents.
Combine ML research engineering and systems-level optimization in one role.
Work on uncharted challenges designing models that can reason plan and adapt autonomously.
Collaborate with a world-class AI team redefining how autonomous systems learn and evolve.

Pay & Work Structure

Youll be classified as an hourly contractor to Mercor.
Paid weekly via Stripe Connect based on hours logged.
Part-time (20 hrs- 40 hrs/week) with fully remote async flexibility work from anywhere on your own schedule.
Weekly Bonus of $500 - $1000 per 5 task created.