ML Engineer (Remote | $100–$150/hr)

About the Opportunity

Responsibilities

• Develop and validate models, training pipelines, inference systems, and supporting infrastructure.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible Python and command-line workflows.
• Work with tensors, automatic differentiation, architectures, tokenization, batching, and generation.
• Optimize latency, throughput, memory usage, and hardware utilization.
• Diagnose numerical instability, memory bottlenecks, distributed failures, and performance regressions.
• Review AI-generated code and design tests, benchmarks, and verification criteria.
• Document technical decisions, trade-offs, implementation details, and limitations.

Required Qualifications

• Master’s degree or PhD in a quantitative discipline such as Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, or Engineering.
• Strong professional or research experience in machine learning.
• Practical proficiency in Python.
• Experience with at least two relevant ML frameworks, libraries, or inference tools.
• Strong understanding of model training, evaluation, numerical computation, or inference.
• Ability to debug ML systems beyond surface-level API usage.
• Experience building reproducible workflows and working independently on complex ML engineering problems.

Relevant Technologies

• Relevant technologies may include PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, and Hugging Face Tokenizers.
• Equivalent tools demonstrating directly relevant technical depth may also be considered.

Preferred Qualifications

• Experience at an established technology company, AI laboratory, research organization, or recognized engineering environment.
• Exceptional open-source contributions or strong academic research experience in ML systems or numerical computing.
• Experience optimizing training or inference, distributed ML systems, hardware utilization, numerical stability, or AI-generated implementations.

Compensation

• Compensation is $100–$150 per hour.
• The engagement is for approximately 15 hours per week as a part-time contractor.
• Compensation is output-based, with experts paid per task that meets project specifications.
• Task completion time may vary based on experience and workflow.

Workplace

• The role is global and fully remote.
• The schedule is flexible, including the option to work weekends.

Eligibility and Process