Machine Learning Research – Engineering Specialist

This is a fully remote position, open to applicants in Brazil, +5 more countries.

📋 Description

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

• Implement or modify model components, data pipelines, evaluation systems, and numerical methods utilizing Python.

• Create reproducible programmatic workflows using Python, command-line tools, and pertinent machine learning frameworks.

• Troubleshoot numerical, algorithmic, and system-level failures to determine root causes and apply dependable solutions.

• Enhance model training and inference for optimal memory usage, computational efficiency, throughput, and overall performance.

• Design and carry out tests to confirm implementation accuracy and adherence to performance objectives.

• Review and validate complex machine learning engineering tasks, including existing code and proposed solutions.

• Utilize coding agents as part of the development process to speed up implementation, debugging, testing, and code review while ensuring the accuracy of generated results.

• Clearly document technical methodologies, implementation choices, test outcomes, and performance insights.

• Deliver high-quality, reproducible task submissions within project timelines.

⛳️ 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 and practical ML engineering.

• Proficient Python programming skills, including the capacity to implement, debug, test, and optimize complex code.

• Practical experience with coding agents within a Python development workflow.

• Comprehension of machine learning model development, training and inference processes, and evaluation techniques.

• Ability to create reproducible implementations and verify correctness through systematic testing.

• Strong quantitative reasoning and problem-solving abilities, particularly in diagnosing numerical or computational challenges.

• Capability to work autonomously, manage technical tasks, and fulfill established quality and submission standards.

• Excellent written communication skills and the capacity to articulate technical findings.

• Availability to contribute approximately 15 hours per week and commence the first task within 24–48 hours following onboarding.

🏝️ Benefits

• Competitive compensation and flexible working hours.

• Opportunities for professional growth and skill development.

• Collaboration with a talented and innovative team.

• Access to cutting-edge tools and technologies in the field of machine learning.