🚀 Machine Learning Expert | AI Training Project

💰 Pay :- ₹9,000–₹14,000/hour
🌍 Location :- Fully Remote
💼 Job Type :- Freelancing | ~15 hours/week
⏰ Schedule :- Flexible — Choose your own working hours and days

🔥 About the Opportunity

We are looking for highly skilled Machine Learning Experts to contribute to an advanced AI Training Project focused on machine learning engineering, model development, training and inference systems, numerical computing, performance optimization and Python.

This opportunity is ideal for experienced ML Engineers, AI Engineers and Machine Learning Researchers who understand the systems behind high-level APIs and can solve complex technical challenges with strong engineering depth.

🧠 What You’ll Work On
Develop and validate ML models, training pipelines and inference systems
Build data pipelines, evaluation frameworks and numerical methods
Implement model components using Python and command-line tools
Work with tensor operations, automatic differentiation, model architectures, tokenization, batching and generation
Optimize ML workloads for latency, throughput, memory usage and hardware utilization
Debug numerical instability, tensor issues, memory bottlenecks and distributed-system failures
Identify and resolve performance regressions and system-level failures
Review and validate AI-generated code and technical solutions
Design tests, benchmarks, and objective verification criteria
Ensure technical solutions are correct, reproducible, efficient and production-ready
Document technical decisions, trade-offs, limitations and implementation approaches

🎯 Required Qualifications
Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering or a related quantitative discipline
Strong professional or research experience in Machine Learning / AI
Strong Python programming skills
Practical experience with at least 2 ML frameworks, libraries or inference tools
Strong understanding of model training, evaluation, inference numerical computation, and optimization
Ability to debug ML systems beyond basic API-level usage
Experience building reproducible technical workflows
Ability to clearly explain implementation decisions, performance trade-offs and failure modes

🛠️ Relevant Technologies

Experience with any of the following is highly relevant :-

PyTorch | JAX | NumPy | SciPy | SGLang | vLLM | llama.cpp | Hugging Face Transformers | Hugging Face Tokenizers

Equivalent technologies may also be considered where candidates demonstrate strong technical depth.

⭐ Preferred Background

Experience at a technology company, AI laboratory, research organization or recognized engineering environment is strongly preferred.

Exceptional open-source contributions, academic research, or advanced independent ML projects may also be considered.

🚀 Who Should Apply?

Ideal for professionals with hands-on expertise in:

Machine Learning • AI Engineering • Deep Learning • Python • Model Training • Model Inference • LLMs • Numerical Computing • ML Optimization • PyTorch • JAX • Hugging Face • Distributed Systems • Performance Engineering • AI Code Evaluation