Remote | ML Engineer — $100–$150/hour
About The Position
This part-time consulting opportunity is for experienced Machine Learning Engineers and researchers contributing to an advanced AI training project. The work focuses on model development, training and inference systems, numerical computing, performance optimisation, and Python-based ML engineering. Selected professionals create, solve, review, and validate technically demanding machine-learning engineering tasks, which may include implementing or modifying models, building reproducible training or inference workflows, optimising performance, debugging numerical or infrastructure-level failures, and verifying correctness and performance requirements. The role involves developing and validating machine-learning models, pipelines, inference systems, and supporting infrastructure; improving training and inference workflows; evaluating AI-generated code and technical solutions; and documenting implementation decisions, trade-offs, and failure modes. Work is remote and open globally, approximately 15 hours per week, with a flexible schedule. Compensation is $100–$150 per hour and output-based, with payment for tasks meeting project specifications. Selected professionals should be prepared to begin their first task approximately 24–48 hours after onboarding. The selection process may include screening questions, an approximately 30-minute AI interview, a technical assessment where required, and hiring-manager review. Work must not use confidential or proprietary information belonging to an employer, client, institution, or other third party.
Requirements
- 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.
- Practical proficiency with Python.
- Meaningful experience with at least two relevant machine-learning frameworks, numerical libraries, or inference tools.
- Strong understanding of model training, evaluation, numerical computation, or inference systems.
- Ability to debug ML systems beyond high-level API usage.
- Ability to explain implementation decisions, performance trade-offs, and failure modes clearly.
- Experience building reproducible technical and programmatic workflows.
- Ability to complete work without using confidential or proprietary information belonging to an employer, client, institution, or other third party.
Nice To Haves
- Experience within an established technology company, AI laboratory, research organisation, or recognised engineering environment is strongly preferred.
- Exceptional open-source or academic experience may also qualify.
- Experience with tools such as PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, Hugging Face Tokenizers, or comparable technologies.
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 technical workflows using Python and command-line tools.
- Work with tensor operations, automatic differentiation, model architectures, tokenisation, batching, and generation.
- Verify that implementations satisfy objective functional, numerical, and performance requirements.
- Optimise training and inference workflows for latency, throughput, memory utilisation, and hardware efficiency.
- Diagnose numerical instability, incorrect tensor behaviour, memory bottlenecks, and performance regressions.
- Analyse system-level failures across model execution and supporting infrastructure.
- Compare alternative implementations and determine whether results are correct, reproducible, and efficient.
- Evaluate trade-offs involving compute, memory, numerical precision, and model performance.
- Review AI-generated code, implementations, and technical solutions for correctness and engineering quality.
- Identify implementation errors, inefficient approaches, weak assumptions, and reproducibility issues.
- Assess whether generated solutions address task requirements; design objective tests, benchmarks, and verification criteria.
- Provide clear written explanations of technical decisions, limitations, and recommended improvements.
- Work with modern machine-learning frameworks, numerical libraries, and inference tooling.
- Debug ML systems beyond surface-level API usage.
- Document implementation decisions, performance trade-offs, and technical failure modes.
- Maintain rigorous and reproducible engineering practices across assigned tasks.
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What This Job Offers
Job Type
Part-time
Career Level
Senior
