Machine Learning Engineer | $14/hr Remote

Crossing Hurdles; India; Remote; INR 1,429,584 - 2,036,075; Part time

Job summary

A technology consulting firm is seeking an Associate to design and deploy machine learning systems in a remote role. You will build deep learning models and develop end-to-end ML pipelines while collaborating with data scientists. The ideal candidate has strong skills in machine learning and Python, with a background in MLOps tools. This position offers flexible, project-based commitments and requires critical evaluation of model performance. Join us to contribute to cutting-edge AI projects in a collaborative environment.

Qualifications

• Strong foundation in machine learning, deep learning, or reinforcement learning.
• Proficiency in Python and experience with ML frameworks.
• Understanding of training infrastructure like GPUs/TPUs.
• Experience building end-to-end ML systems.
• Ability to evaluate model performance critically.

Responsibilities

• Design, train, and deploy scalable machine learning systems.
• Build and fine-tune deep learning models.
• Develop end-to-end ML pipelines.
• Implement reinforcement learning workflows.
• Collaborate with data scientists to optimize data quality.

Skills

Machine Learning

Deep Learning

Reinforcement Learning

Python

MLOps

Tools

PyTorch

TensorFlow

JAX

Docker

Kubernetes

Airflow

Job description

Base pay range: $14.00/hr - $14.00/hr

Type: Hourly contract

Compensation: $14/hour

Location: Remote

Commitment: Flexible, project-based

Role Responsibilities

• Design, train, and deploy scalable machine learning systems supporting autonomous AI agents.
• Build and fine-tune deep learning models for reasoning, code generation, and real-world decision‑making tasks.
• Develop end‑to‑end ML pipelines covering data preprocessing, feature extraction, training, evaluation, and deployment.
• Implement reinforcement learning workflows and self‑improving training loops to enhance agent adaptability.
• Create benchmarking and evaluation tools to measure model accuracy, reasoning ability, and performance.
• Collaborate with data scientists and systems engineers to optimize data quality, inference speed, and hardware utilization.
• Maintain model reproducibility, version control and experiment tracking using MLOps best practices.
• Contribute to research efforts focused on improving learning efficiency, generalization, and model robustness.

Requirements

• Strong foundation in machine learning, deep learning, or reinforcement learning.
• Proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Understanding of training infrastructure, including GPUs/TPUs, distributed training and data pipelines.
• Experience building end‑to‑end ML systems from experimentation through deployment.
• Familiarity with MLOps tools such as Weights & Biases, MLflow, Docker, Kubernetes or Airflow.
• Experience designing custom model architectures or working with transformer‑based systems, LLMs or diffusion models.
• Ability to evaluate model performance critically using data‑driven experimentation.
• Interest in AI agents, autonomous reasoning, and collaborative model behavior.

Application Process (Takes 20 Min)

• Upload resume
• Interview (15 min)

Seniority level

Associate

Employment type

Contract

Job function

Engineering and Information Technology

Industries

Software Development and IT Services and IT Consulting