Senior AI Engineer - Reinforcement Learning (Post-training)

Employment Type: 6 Month Contract

We are seeking a highly skilled Senior AI Engineer to accelerate the deployment of improvements to our models. You will collaborate with diverse teams handling various facets of the system, including core capabilities, multimodal integration (code, text, and images), tools, and more. This role offers a unique opportunity to shape the future of the Internet Computer, working across the technology stack, from optimizing low-level components like GPU kernels to mastering the intricacies of reinforcement learning post-training.

The ideal candidate has a robust technical background in areas such as data technologies, reliable software engineering, production ML model development, and cross-functional collaboration. While research experience is not required, a deep understanding of ML fundamentals and large-scale deep learning is essential for troubleshooting and analyzing complex system and ML issues. Excellent verbal and written communication skills, along with strong project management abilities, are crucial as you will collaborate with both technical engineering and research teams and non-technical product teams across the company.

Responsibilities

• Ownership of Post-Training Pipeline: Lead the design, implementation, and optimization of the post-training pipeline to ensure efficient model deployment and scalability.
• Coordination of Data Development: Oversee the development of high-quality training datasets, including managing the creation and use of synthetic data.
• Model Training: Conduct advanced model training, ensuring continuous improvement in accuracy and performance.
• Collaboration: Work closely with cross-functional teams including data engineers, software engineers, and product teams to integrate AI models into production.
• Performance Monitoring: Analyze and monitor the performance of models in production, iterating on training pipelines to enhance outcomes.

Requirements

• Experience: Minimum 5 years of experience in AI/ML engineering with a focus on model training and deployment.
• Post-Training Expertise: Demonstrated ability to build and optimize post-training pipelines at scale.
• Data Coordination: Experience in managing the development and annotation of synthetic and real-world datasets.
• Technical Skills: Proficiency in Python, TensorFlow/PyTorch, and experience with cloud platforms like AWS, GCP, or Azure.
• Team Leadership: Proven track record of coordinating complex engineering projects with cross-functional teams.
• Analytical Skills: Strong problem-solving skills with a focus on performance optimization and automation.

Bonus Points

• Prior experience with distributed AI systems.
• Hands-on experience with synthetic data generation and augmentation techniques.
• Familiarity with tools for data pipeline automation and orchestration.