Senior ML Engineer (Remote)

Job summary

Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure. Implement model components, data and evaluation pipelines, and numerical methods, while building reproducible Python workflows and verifying correctness and performance.

Job details

Role Title: ML Engineer (MSc. or PhD) Role Type: Contractor (~15 hours per week) Location: Global, fully remote, Senior ML Engineer (MSc. or PhD) Pay: $60–$120/hour Schedule: Flexible—you choose the hours and days you work, including weekends if desired We are looking for highly skilled Machine Learning Experts to contribute to an AI training project involving model development, training and inference systems, numerical computing, performance optimization, and Python. The work involves creating, solving, reviewing, and validating challenging machine-learning engineering tasks. A representative task may require implementing or modifying a model, constructing a reproducible training or inference workflow, optimizing memory or throughput, debugging numerical or system-level failures, and verifying that the resulting implementation satisfies objective correctness and performance requirements. This is a very coding heavy role. Candidates but have experience using coding agents with python in their workflow. What You’ll Work On 1. Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure. 2. Implement model components, data pipelines, evaluation systems, and numerical methods. 3. Build reproducible programmatic workflows using Python and command-line tools. Required Qualifications 1. A master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline. 2. Strong professional or research experience in machine learning. 3. Practical proficiency with Python and coding agents. Equivalent tools may also be considered when the candidate demonstrates directly relevant depth. Experience at a well-established technology company, AI laboratory, research organization, or other recognized engineering environment is strongly preferred. Exceptional open-source or academic experience may also qualify. Process 1. Apply to the role and complete the screening questions. 2. Complete an AI interview of approximately 30 minutes. 3. Complete the hiring manager review. Compensation Structure Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Start Timeline & Availability 1. We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding. What You’ll Do 1. Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure. 2. Implement model components, data pipelines, evaluation systems, and numerical methods. 3. Build reproducible programmatic workflows using Python and command-line tools. Apply Now!

What you’ll do

Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure. Implement model components, data and evaluation pipelines, and numerical methods, while building reproducible Python workflows and verifying correctness and performance.

Requirements

A master’s degree or PhD in a relevant quantitative discipline and strong professional or research experience in machine learning are required. Candidates should have practical proficiency with Python and coding agents; experience in a recognized technology, AI, research, or engineering organization is preferred.

Listed skills

• Machine learning · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Python
• Coding Agents
• Model Development
• Model Training
• Inference Systems
• Numerical Computing
• Performance Optimization
• Training Pipelines
• Data Pipelines
• Evaluation Systems
• Numerical Methods
• Command-Line Tools
• Debugging
• Reproducible Workflows

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Science & Research

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