ML Engineer

Role Overview

Contribute expert machine learning engineering work to projects that develop, train, evaluate, and improve AI systems. You will create, solve, review, and validate challenging technical tasks spanning model development, training and inference systems, numerical computing, and performance optimization.

Key Responsibilities

• Develop and validate machine learning models, training pipelines, inference systems, and supporting infrastructure.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible workflows using Python and command-line tools.
• Work with tensor operations, automatic differentiation, model architectures, tokenization, batching, and generation.
• Optimize training and inference for latency, throughput, memory use, and hardware utilization.
• Diagnose numerical instability, incorrect tensor behavior, memory bottlenecks, distributed-system failures, and performance regressions.
• Compare model implementations to determine whether results are correct and reproducible.
• Review AI-generated code and technical solutions for correctness, efficiency, and engineering quality.
• Design objective tests, benchmarks, and verification criteria, and document technical decisions, trade-offs, and limitations.

Qualifications

• Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
• Strong professional or research experience in machine learning, with practical Python proficiency.
• Meaningful experience with at least two relevant machine learning frameworks, libraries, or inference tools.
• Strong understanding of model training, evaluation, numerical computation, or inference.
• Ability to debug machine learning systems beyond surface-level API use and clearly explain implementation decisions, performance trade-offs, and failure modes.
• Experience building reproducible technical workflows.
• Relevant tools may include PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, and Hugging Face Tokenizers. Equivalent tools are considered when supported by directly relevant depth.
• Experience at an established technology company, AI lab, research organization, or other recognized engineering environment is strongly preferred.

Work Terms

• Global, fully remote independent contractor engagement.
• Approximately 15 hours per week.
• Flexible schedule, with the ability to choose working hours and days, including weekends if desired.

Compensation

$100 to $150 per hour.