Machine Learning Engineer (3 Month Intern)

As a Machine Learning Software Engineer Intern, you will develop computer vision and machine learning algorithms for autonomous vehicles.

Key Responsibilities:

• Model Adaptation & Integration: Adapt open-source foundation models (e.g., LLMs, vision transformers, multimodal models) to run efficiently on our custom GPU inference infrastructure.

• Performance Optimization: Identify bottlenecks in model inference pipelines, implement GPU kernels, and optimize code to reduce latency and improve throughput.

• Platform Tooling & Automation: Develop scripts and tooling for automating model conversion, quantization, and configuration processes to streamline deployment workflows.

• Testing & Validation: Implement benchmarking tests and validation suites to ensure model accuracy, reliability, and performance meet internal standards.

• Collaboration with Cross-Functional Teams: Work closely with machine learning researchers, MLOps engineers, and infrastructure teams to refine performance strategies and ensure smooth integration of foundation models into production environments.

• Documentation & Knowledge Sharing: Document adaptation procedures, best practices, and lessons learned. Contribute to internal knowledge bases and present findings in team meetings.

Qualifications:

• Educational Background: Currently pursuing a Graduate degree in Computer Science, Electrical Engineering, or a related technical field.

• Programming Skills: Proficiency in Python and familiarity with go and CUDA is a plus.

• Foundational Knowledge in Machine Learning: Understanding of attention based models, PyTorch, and GPU-accelerated computing.

• Problem-Solving Mindset: Strong analytical skills, with the ability to troubleshoot performance issues and propose innovative optimization strategies.

• Team Player: Excellent communication skills, eagerness to learn, and the ability to collaborate effectively with diverse teams.