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Machine Learning Researcher, Internship

Palantir

Washington, D.C, USA

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8 w

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Internship

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Other

The Role
We are looking for research interns to join our Machine Learning Team at Palantir. Our team develops novel machine learning techniques to address immediate customer needs and to ensure Palantir’s platforms offer unparalleled machine learning capabilities.

Ideal candidates are graduate students that are passionate about computer vision, natural language processing and deep learning. If you like to push the boundaries of what is possible, and want to address hard problems on top of unique datasets, this is the right opportunity for you! You will help us push the research boundaries across 3 different areas:

Edge AI: Implementing and customizing novel techniques to enable efficient object detection model inference on edge hardware ranging from ARM and Intel CPU architectures all the way to Jetson NVIDIA GPUs. Exploring both software-only and hardware-aware approaches to optimize the latest object detection architectures on edge hardware.

Few-shot learning and vision-language models: Driving novel few-shot learning solutions using the latest vision-language models to enable accurate object detection in overhead aerial imagery.

Natural Language Processing (NLP): Enabling end-to-end solutions in entity, event and relationship extraction and classification, as well as in the areas of structured text extraction from documents.

Technologies We Use
• Python
• PyTorch
• ONNX
• TensorRT
• Hugging Face
• Docker

Core Responsibilities
• Read, present and implement research papers in the relevant research areas of interest
• Modify and/or invent techniques in object detection and inference optimization to optimize performance on resource constrained hardware such as ARM, Intel CPUs and NVIDIA Jetson GPUs
• Fine-tune vision language models on unique overhead aerial imagery datasets
• Modify and/or invent techniques in entity, event and relationship extraction/classification, and in the area of structured text extraction from documents

What We Value
• Experience with software-only and/or hardware-aware techniques for model inference optimization on edge devices
• Background in Computer Vision Deep Learning and Object Detection in particular
• Experience with TensorRT, ONNX and other optimization frameworks such as TVM and OpenVINO
• Experience with implementing or fine-tuning innovative deep learning models in the areas of NER and structure text extraction from documents
• Background in NLP and Deep Learning
• Experience with few-shot learning techniques
• Experience tuning vision-language models such as CLIP or GLIP
• Proficiency with any of the major Deep Learning frameworks
• BS in Computer Science or related fields. Candidates are expected to be enrolled students in a graduate program (MS or PhD).
• Excellent communication and collaboration skills