Machine Learning Researcher (MSc/PhD)
About the Role
A high-impact AI initiative is seeking advanced machine learning expertise to develop, evaluate, and optimize model training and inference systems. The work focuses on practical ML engineering, numerical computing, Python-based implementation, and performance optimization.
This opportunity is suited to researchers and engineers with a strong quantitative background, advanced machine learning knowledge, and substantial hands-on coding experience. Candidates must be comfortable integrating AI coding agents into their development workflow.
The work involves designing and implementing challenging technical tasks, debugging model and system failures, building reproducible workflows, and validating results against objective correctness and performance criteria. Technical depth, reproducibility, and implementation quality are critical.
What You'll Do
- Develop and validate machine learning models, training pipelines, and inference systems.
- Implement model components, data pipelines, evaluation frameworks, and numerical methods.
- Optimize memory utilization, computational throughput, and system performance.
- Debug numerical errors, model failures, and infrastructure-level issues.
- Build reproducible Python workflows using command-line tools.
- Create, solve, review, and validate complex machine learning engineering tasks.
- Use coding agents to support implementation, debugging, and development workflows.
- Verify deliverables against defined correctness, performance, and submission requirements.
Requirements
- A 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.
- Advanced practical proficiency in Python.
- Demonstrated experience using AI coding agents in software development workflows.
- Ability to implement, debug, test, and optimize complex technical solutions independently.
- Strong understanding of reproducible experimentation, numerical correctness, and performance evaluation.
- Ability to work remotely with flexible scheduling, including approximately 15 hours per week.
- Availability to begin the first task within 24–48 hours of completing onboarding, if selected.
- Willingness to work under an output-based compensation model in which payment depends on tasks meeting project specifications.
- Experience at an established technology company, AI laboratory, research organization, or comparable engineering environment is strongly preferred.
- Exceptional academic research, open-source contributions, or equivalent technical achievements are also valued.