ML Engineer
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Job Description
Machine Learning Engineer developing, optimising, and reviewing AI training and inference systems for 24-MAG’s global remote consulting platform. Part-time contractor role focused on Python-based ML engineering.
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 implementations against 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 for correctness, reproducibility, and efficiency
- 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 generated solutions against task requirements
- Design objective tests, benchmarks, and verification criteria
- Provide clear written explanations of technical decisions, limitations, and recommended improvements
- Apply practical understanding of model training, evaluation, numerical computation, and inference systems
- 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
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
- Relevant tools may include PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, Hugging Face Tokenizers, or comparable technologies
- 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
- Must be prepared to begin the first task within approximately 24–48 hours of completing onboarding
- Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
Benefits:
- Part-time independent contractor engagement
- Fully remote and open globally
- Approximately 15 hours per week
- Flexible schedule, including the ability to choose working days and hours
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