Machine Learning Engineer - India

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’ll be working at the intersection of ML research, systems engineering, and AI agent behavior — transforming ideas into robust, scalable learning pipelines.

You’re 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. You’ll design model architectures, training workflows, and evaluation pipelines that push the frontier of autonomous intelligence and real-time reasoning.

What You’ll 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

• Weekly Bonus of $500 - $1000 per 5 task created.

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

Contract and Payment Terms

• You will be engaged as an independent contractor.
• This is a fully remote role that can be completed on your own schedule.
• Projects can be extended, shortened, or concluded early depending on needs and performance.
• Your work at Mercor will not involve access to confidential or proprietary information from any employer, client, or institution.
• Payments are weekly on Stripe or Wise based on services rendered.
• Please note: We are unable to support H1-B or STEM OPT candidates at this time.

About Mercor

Mercor partners with leading AI labs and enterprises to train frontier models using human expertise. You will work on projects that focus on training and enhancing AI systems. You will be paid competitively, collaborate with leading researchers, and help shape the next generation of AI systems in your area of expertise.