ML Researcher | Remote

The Ai Training Company; India; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

The Ai Training Company is seeking experienced Machine Learning Engineers, ML Researchers, AI Engineers, and ML Systems Engineers to contribute to a technically challenging AI training project.

You will develop and validate models, build reproducible training and inference workflows, optimize for latency and throughput, and document technical decisions and tradeoffs. Collaboration with cross-functional teams will be essential as you tackle distributed training and large language model challenges.

Qualifications

• Master’s degree or PhD in Computer Science, Machine Learning, AI, or related quantitative field.
• Strong professional or research experience in machine learning.
• Strong proficiency with Python.
• Experience with at least two modern ML frameworks/libraries.
• Ability to debug ML systems beyond basic API use.

Responsibilities

• Develop and validate machine learning models and components.
• Build reproducible training and inference workflows.
• Implement model architectures, tensor operations, data pipelines, and generation systems.
• Review and debug AI-generated ML code.
• Diagnose numerical instability and memory issues.
• Optimize inference/training for latency and throughput.
• Build benchmarks and evaluation criteria.
• Develop tests validating model behavior and performance.
• Debug distributed and system-level ML failures.
• Document technical decisions and tradeoffs.

Skills

Python

Machine Learning

Deep Learning

Research

Communication

Documentation

Education

PhD in CS/ML

Master's degree

Tools

PyTorch

JAX

TensorRT-LLM

llama.cpp

CUDA

Job description

Machine Learning Engineers, ML Systems & Inference Experts.

We are seeking experienced Machine Learning Engineers, ML Researchers, AI Engineers, ML Systems Engineers, Inference Engineers, and Performance Optimization Specialists to contribute to a technically challenging AI training project.

What You’ll Do

• Develop and validate machine learning models and components
• Build reproducible training and inference workflows
• Implement model architectures, tensor operations, data pipelines, tokenization, batching, and generation systems
• Review and debug AI-generated machine learning code
• Diagnose numerical instability, incorrect tensor behavior, memory issues, and performance regressions
• Optimize inference and training for latency, throughput, memory consumption, and hardware utilization
• Compare multiple model implementations for correctness, efficiency, and reproducibility
• Build benchmarks and objective evaluation criteria
• Develop tests that verify model behavior and performance
• Debug distributed and system-level ML failures
• Document technical decisions, engineering tradeoffs, assumptions, and limitations
• Create reference implementations and technically rigorous solutions

Who Can Apply

Relevant backgrounds include:

Machine Learning Engineers, Senior Machine Learning Engineers, Staff Machine Learning Engineers, Principal Machine Learning Engineers, Lead Machine Learning Engineers, Applied Machine Learning Engineers, and ML Software Engineers.

We also welcome:

AI Engineers, Senior AI Engineers, Artificial Intelligence Engineers, Applied AI Engineers, Generative AI Engineers, Deep Learning Engineers, Neural Network Engineers, and AI Systems Engineers.

Research backgrounds may include:

Machine Learning Researchers, Research Engineers, AI Research Engineers, Research Scientists, Applied Scientists, Deep Learning Researchers, NLP Researchers, Computer Vision Researchers, Reinforcement Learning Researchers, and Foundation Model Researchers.

ML systems backgrounds may include:

Performance-focused backgrounds may include:

Performance-focused backgrounds may include:

ML Performance Engineers, AI Performance Engineers, GPU Performance Engineers, Systems Performance Engineers, Model Optimization Engineers, Deep Learning Performance Engineers, HPC Engineers, High-Performance Computing Engineers, and Performance Optimization Specialists.

LLM-focused backgrounds may include:

Additional relevant backgrounds include:

Additional relevant backgrounds include:

NLP Engineers, Computer Vision Engineers, Speech ML Engineers, Recommendation Engineers, Ranking Engineers, Search Engineers, Data Scientists, Quantitative Researchers, MLOps Engineers, Backend Engineers, Systems Engineers, Distributed Systems Engineers, and Scientific Computing Engineers with strong hands-on machine learning experience.

Strong candidates may have experience with:

• Supervised learning
• Self-supervised learning
• Deep learning
• Batch inference
• Online inference
• Distributed inference
• Distributed training
• Multi-GPU training
• Evaluation pipelines
• Data preprocessing
• Checkpointing
• Memory optimization
• Throughput optimization
• Latency optimization

Requirements

• Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, Physics, or a closely related quantitative discipline
• Strong professional or research experience in machine learning
• Strong practical proficiency with Python
• Meaningful experience with at least two modern ML frameworks, libraries, or inference tools
• Strong understanding of machine learning model training, evaluation, numerical computation, or inference
• Ability to debug ML systems beyond basic high-level API usage
• Ability to understand tensor operations, model behavior, and implementation details
• Experience creating reproducible technical workflows
• Ability to evaluate whether an implementation is objectively correct
• Strong technical communication and documentation skills
• Ability to clearly explain implementation decisions, performance tradeoffs, and failure modes

Preferred Background

• Experience at a major technology company, AI lab, research organization, or engineering-intensive company
• Experience training or serving large neural networks
• Experience working with large language models
• Experience with PyTorch and JAX
• Experience with vLLM, SGLang, llama.cpp, TensorRT-LLM, or similar inference frameworks
• Experience with GPU optimization or CUDA
• Experience with distributed training or inference
• Experience implementing ML components rather than only calling high-level APIs
• Experience reviewing ML code written by other engineers or researchers
• Experience designing ML benchmarks, evaluations, or technical assessments
• Experience profiling and optimizing production machine learning systems
• Significant open-source machine learning contributions
• Strong academic ML research experience
• Published research in machine learning, AI, numerical computing, or related areas

This opportunity is ideal for experienced machine learning engineers and researchers who understand models at the implementation and systems level, can debug difficult ML failures, and can determine whether a solution is correct, performant, reproducible, and professionally engineered. We are a referral partner of the client.