Remote ML Engineer (Contract) — Build & Optimize Pipelines

Crossing Hurdles; United States; Remote; GBP 102,000 - 153,000; Part time

Job summary

Crossing Hurdles is seeking an ML Engineer for a remote contract role. You will develop and validate machine-learning models, training pipelines, and inference systems, building reproducible Python-based workflows.

The position emphasizes optimizing latency, throughput, memory usage, and hardware utilization, with a focus on diagnosing numerical issues, memory bottlenecks, and distributed-system failures.

Qualifications

• Master's degree or PhD in Computer Science, ML, AI, applied mathematics, statistics, engineering or closely related quantitative discipline.
• Strong professional or research experience in machine learning.
• Practical proficiency with Python and at least two ML frameworks or inference tools.

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 programmatic workflows using Python and command-line tools.
• Optimize training or inference for latency, throughput, memory usage, and hardware utilization.
• Diagnose numerical instability, incorrect tensor behavior, memory bottlenecks, distributed-system failures, and performance regressions.

Skills

ML experience

Python proficiency

Debug ML systems

Education

Master's degree or PhD in Computer Science / ML / AI / Applied Mathematics / Statistics / Engineering

Tools

Python

ML frameworks (TensorFlow, PyTorch)

Job description

Crossing Hurdles is seeking an ML Engineer for a remote contract role. You will develop and validate machine-learning models, training pipelines, and inference systems, building reproducible Python-based workflows.

The position emphasizes optimizing latency, throughput, memory usage, and hardware utilization, with a focus on diagnosing numerical issues, memory bottlenecks, and distributed-system failures.