Senior Machine Learning Engineer — AI Systems
About the Role
A structured AI initiative is seeking experienced machine learning engineers to develop, evaluate, and optimize challenging systems spanning model training, inference, numerical computing, and performance engineering.
This opportunity is ideal for experienced ML engineers and researchers with strong Python skills, practical knowledge of modern machine learning frameworks, and the ability to reason below high-level APIs. Advanced academic or industry experience in machine learning, AI, computer science, applied mathematics, statistics, or a related quantitative field is preferred.
The work involves building reproducible ML workflows, debugging complex model and systems behavior, optimizing performance, and reviewing technical implementations against objective correctness and efficiency criteria, where technical depth and rigorous validation are critical.
What You'll Do
- 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 usage, and hardware utilization
- Diagnose numerical instability, tensor errors, memory bottlenecks, distributed-system failures, and performance regressions
- Compare implementations and verify correctness and reproducibility
- Review AI-generated code and technical solutions for correctness, efficiency, and engineering quality
- Design tests, benchmarks, and objective verification criteria
- Document technical decisions, trade-offs, and system limitations
Requirements
- Strong professional or research experience in machine learning
- Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative field
- Practical proficiency with Python
- Meaningful 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 machine-learning systems beyond surface-level API usage
- Experience building reproducible technical workflows
- Ability to clearly explain implementation decisions, performance trade-offs, and failure modes
- Experience with tools such as PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, or Hugging Face Tokenizers
- Equivalent ML tooling experience may be considered where technical depth is directly relevant
- Ability to work approximately 15 hours per week on a flexible schedule
- Ability to work independently in a fully remote environment
- Availability to begin promptly following onboarding
- Preferred: Experience in established technology, AI research, engineering, or open-source environments; advanced experience with model optimization, inference infrastructure, distributed systems, or AI-generated code evaluation