As an Applied Research Scientist, you will innovate and translate cutting edge research into user experiences. If you find yourself thinking about any of these questions:
- How to prompt a model like GPT-4 effectively?
- How to build a foundation model for a specific domain like medical records?
- How to use models like LayoutLM V3 for weakly labeling documents at scale?
- How to blend prompt engineering, retrieval augmentation, and fine-tuning to customize models with the least human time and effort?
We’re looking for a talented researcher to join the team to work on foundational multimodal problems with the focus on data development techniques.
Main Responsibilities
- Establish and empirically demonstrate the state-of-the-art approaches for data-centric model iteration and analysis
- Prototype end-to-end workflows with novel techniques and algorithms, synthesize results, and help to transfer learnings into Snorkel products
- Work closely with design partners to validate your work on real-world use cases with measurable impact
- Contribute to novel research on topics of interest to Snorkel AI by collaborating with other Snorkel Research scientists and affiliate scientists (academic, government, and industry researchers)
- Work a hybrid schedule in our Redwood City HQ and work remotely with "No Meeting" Tuesdays and Thursdays
Preferred Qualifications
- PhD + 2+ YOE working on applied research on Computer Vision tasks
- Experience with 3D Computer vision
- Experience handling large scale medical imaging datasets including CT-Scan or robotics LiDAR datasets.
- Experience in delivering one or more of these domains: Large vision language models, visual question answering, image-text alignment, text to image, text to video, text to motion, image to video, image captioning, activity/action recognition, Augmented and Virtual reality, object detection, semantic segmentation, depth estimation, trajectory prediction, pose estimation, keypoint detection and tracking, medical vision (MRI, CT-Scan, X-ray), perception (robotics/autonomous agents).
- Experience prototyping, experimenting, and testing with large scale datasets and training deep models.
- Experience with standard machine learning frameworks and tools (NumPy, Scikit-learn, Pandas, Pytorch, TensorFlow, etc.) and machine learning cloud infrastructure and accelerators (AWS, Google Cloud, GPUs and TPUs).
- Strong technical communication skills and the ability to work in a fast-paced environment.
- Experience with developing robust software with excellent coding hygiene and modular design.
- The position involves working with problems with no off-the-shelf solutions and requires innovation on-the-fly. Typically a Ph.D. in machine learning or a related area with good publication history would be a good fit for this position. We would also love to hear from people with similar skill sets acquired through other career paths.
