AI Research Scientist (Remote | $60–$120/hr)

Synthires; California; Remote; USD 110,000 - 193,000; Part time

Benefits offered by this job

Fully remote

Flexible working arrangements

Hourly contract engagement

Job summary

Synthires is seeking experienced Machine Learning Engineers to design, train, and evaluate next‑generation AI systems. You will work with Python, ML frameworks, and MongoDB to build scalable models and data pipelines in a fully remote environment.

You will analyze large datasets, tune hyperparameters, and document experiments to ensure reproducibility, collaborating with cross‑functional teams on cloud‑ready workflows and production ML deployments.

Qualifications

• Professional experience as a Machine Learning Engineer, ML Developer, Data Scientist, or similar role.
• Strong proficiency in Python and machine learning development.
• Hands-on experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.

Responsibilities

• Design, develop, and refine machine learning models using Python and ML frameworks.
• Analyze and process large datasets for model training, validation, and evaluation.
• Use MongoDB for data storage, manipulation, querying, and retrieval.
• Develop and maintain data preprocessing and feature engineering pipelines.
• Evaluate model performance using appropriate metrics, benchmarking, and validation techniques.
• Perform hyperparameter tuning and iterative model optimization.
• Collaborate with cross-functional teams to identify opportunities for model and workflow improvements.
• Integrate data pipelines into training and inference workflows.
• Document experiments, methodologies, results, and technical decisions for reproducibility.
• Provide actionable insights and recommendations based on ML findings.

Skills

Machine Learning Engineer

Python

Data analysis

Model development

MongoDB

Remote work

Tools

scikit-learn

TensorFlow

PyTorch

Job description

This opportunity is for experienced Machine Learning Engineers with expertise in Python, machine learning, data analysis, model development, and MongoDB to contribute to advanced AI training and evaluation projects.

You’ll apply your technical expertise to design and refine machine learning solutions, analyze datasets, evaluate model performance, and develop reliable workflows that support the training and deployment of next-generation AI systems.

Responsibilities

• Design, develop, and refine machine learning models using Python and relevant ML frameworks.
• Analyze and process large datasets for model training, validation, and evaluation.
• Use MongoDB for efficient data storage, manipulation, querying, and retrieval.
• Develop and maintain data preprocessing and feature engineering pipelines.
• Evaluate model performance using appropriate metrics, benchmarking, and validation techniques.
• Perform hyperparameter tuning and iterative model optimization.
• Collaborate with cross-functional teams to identify opportunities for model and workflow improvements.
• Integrate data pipelines into training and inference workflows.
• Document experiments, methodologies, results, and technical decisions to ensure reproducibility.
• Provide actionable insights and recommendations based on machine learning and data-driven findings.

Required Qualifications

• Professional experience working as a Machine Learning Engineer, ML Developer, Data Scientist, or similar role.
• Strong proficiency in Python and machine learning development.
• Hands‑on experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Practical experience using MongoDB for data management and retrieval.
• Strong understanding of machine learning algorithms, model evaluation, and data preprocessing.
• Experience with feature engineering, hyperparameter tuning, and model benchmarking.
• Strong analytical and problem‑solving abilities.
• Excellent written communication and technical documentation skills.
• Ability to work independently and collaborate effectively in a remote environment.

Preferred Qualifications

• Experience deploying or operationalizing machine learning models in cloud or enterprise environments.
• Familiarity with ML pipelines, model serving, and inference workflows.
• Experience working with large-scale datasets and production ML systems.
• Knowledge of MLOps, model monitoring, or automated ML workflows.
• Experience contributing to AI training, model evaluation, or data-quality initiatives.
• Strong understanding of scalable data and machine learning architectures.
• Ability to adapt quickly to evolving technical requirements and project priorities.

Compensation

• Competitive compensation of $80-$140/hour.
• Hourly contract engagement.
• Fully remote with flexible working arrangements.
• Opportunity to contribute to next‑generation AI training and evaluation projects.