Remote | ML Engineer

We are sharing a specialised part‑time consulting opportunity for experienced Machine Learning Engineers and researchers to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, performance optimisation, and Python‑based ML engineering.

Selected professionals will create, solve, review, and validate technically demanding machine‑learning engineering tasks. The work may involve implementing or modifying models, building reproducible training or inference workflows, optimising system performance, debugging numerical or infrastructure‑level failures, and verifying that solutions meet objective correctness and performance requirements.

• 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 appropriately address the technical requirements of each task
• 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
• 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

• 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, Num Py, Sci Py, SGLang, vLLM, llama.cpp, Hugging Face Transformers, Hugging Face Tokenizers, or comparable technologies
• Experience with in an established technology company, AI laboratory, research organisation, or recognised engineering…