ML Engineer

This is a fully remote position, open to applicants in New York.

📋 Description

• Develop and validate machine learning models, training pipelines, inference systems, and the necessary supporting infrastructure.

• Implement components of models, data pipelines, evaluation systems, and numerical methods.

• Create reproducible technical workflows utilizing Python and command-line tools.

• Engage with tensor operations, automatic differentiation, model architectures, tokenization, batching, and generation.

• Ensure implementations meet objective functional, numerical, and performance standards.

• Optimize training and inference workflows for factors such as latency, throughput, memory usage, and hardware efficiency.

• Diagnose issues related to numerical instability, improper tensor behavior, memory bottlenecks, and performance regressions.

• Analyze failures at the system level throughout model execution and its supporting infrastructure.

• Compare different implementations for correctness, reproducibility, and efficiency.

• Weigh trade-offs concerning compute resources, memory, numerical precision, and model performance.

• Review AI-generated code, implementations, and technical solutions to ensure correctness and quality of engineering.

• Identify errors in implementation, inefficient methods, flawed assumptions, and issues with reproducibility.

• Evaluate generated solutions against the specified task requirements.

• Design objective tests, benchmarks, and criteria for verification.

• Provide clear written explanations regarding technical decisions, limitations, and suggested improvements.

• Apply a practical understanding of model training, evaluation, numerical computation, and inference systems.

• Debug machine learning systems beyond surface-level API interactions.

• Document decisions made during implementation, performance trade-offs, and technical failure modes.

• Uphold stringent and reproducible engineering practices across all assigned tasks.

⛳️ Requirements

• Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative field.

• Significant professional or research experience in machine learning.

• Practical proficiency in Python programming.

• Substantial experience with at least two relevant machine-learning frameworks, numerical libraries, or inference tools.

• Deep understanding of model training, evaluation, numerical computation, or inference systems.

• Capability to debug machine learning systems beyond high-level API usage.

• Ability to articulate implementation decisions, performance trade-offs, and failure modes effectively.

• Experience in constructing reproducible technical and programmatic workflows.

• Familiarity with tools such as PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, Hugging Face Tokenizers, or similar technologies.

• Experience in a reputable technology company, AI lab, research organization, or respected engineering environment is highly preferred.

• Exceptional open-source or academic experience may also be considered.

• Must be ready to commence the first task within approximately 24–48 hours after onboarding completion.

• Work must be performed without utilizing any confidential or proprietary information belonging to any employer, client, institution, or other third parties.

🏝️ Benefits

• Engagement as a part-time independent contractor.

• Fully remote position, open to candidates globally.

• Approximately 15 hours of work each week.

• Flexible schedule, allowing for the selection of working days and hours.