Key Responsibilities Include:

• Onboard, evaluate, and implement open-source machine learning models.
• Build scalable data ingestion, processing, and curation pipelines for AI model development.
• Develop Python scripts to download, parse, filter, and process large datasets from the web.
• Write and optimize SQL queries for large-scale data transformation and analysis.
• Implement published machine learning research and translate research concepts into functional systems.
• Support the development and evaluation of multimodal AI models, particularly video-language and image-language models.
• Perform qualitative and technical analysis of datasets used for model training.
• Troubleshoot GPU utilization, memory, performance, and machine learning infrastructure issues.
• Optimize ML workflows for scalability, reliability, and computational efficiency.
• Write high-quality, maintainable, efficient, and well-documented code.
• Participate in code reviews and help establish engineering and research best practices.
• Collaborate with research, ML infrastructure, software engineering, and data teams.
• Communicate technical findings, project risks, and progress to cross-functional stakeholders.
• Independently own technical deliverables while contributing effectively within a collaborative research environment.

Required Qualifications:

• Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, Software Engineering, or a related technical discipline.
• 6+ years of relevant experience, or equivalent expertise, in machine learning research engineering, ML engineering, systems engineering, or software engineering involving ML implementation.
• At least 2 years of recent, hands-on machine learning development experience.
• At least 2 years of experience processing large-scale datasets or building data-intensive systems.
• Strong programming proficiency in Python.
• Hands-on research or production experience with PyTorch.
• Proficiency in writing and optimizing SQL queries.
• Experience with Linux environments and shell scripting.
• Experience building data pipelines, data curation workflows, or large-scale data processing systems.
• Experience implementing machine learning models, research methods, or open-source ML projects.
• Experience with computer vision, multimodal AI, vision-language models, video-language models, image-language models, or large language models.
• Strong understanding of data structures, algorithms, debugging, and system design.
• Experience owning or leading cross-functional technical projects.
• Strong analytical, problem-solving, and attention-to-detail skills.
• Excellent written and verbal communication skills.
• Ability to work independently and collaborate effectively with research and engineering teams.

Preferred Qualifications:

• Master’s degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, Computer Vision, or a related field.
• Combined academic research and industry experience.
• Experience as a Machine Learning Research Engineer, Research Scientist, Applied Scientist, or ML Infrastructure Engineer.
• Research experience involving video-language models, video understanding, or multimodal video AI.
• Experience with image-language models, visual representation learning, image generation, or other computer vision applications.
• Publications or presentations at leading computer vision or machine learning venues, such as CVPR, ICCV, ECCV, IJCV, or NeurIPS.
• Experience implementing published research papers or reproducing state-of-the-art research results.
• Contributions to open-source machine learning frameworks, models, or research repositories.
• Experience with GPU systems, CUDA, memory debugging, distributed training, or performance optimization.
• Experience with machine learning infrastructure, model training systems, model serving, or ML backend platforms.
• Experience with web-scale data collection, web scraping, dataset parsing, or large-scale data filtering.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with MLOps, DevOps, CI/CD, and infrastructure automation.
• Experience working in a large-scale technology organization, advanced AI laboratory, or highly regarded academic research environment.