Slyce is the market leader in the emerging technology of image recognition and visual search. Over the past few years, we've been busy raising capital, growing our team, and signing deals with 25+ of the leading retailers in the US including Home Depot, Bed Bath & Beyond, Neiman Marcus, and Macy's. We are a close-knit team with ambitious goals and we're excited to drive our cutting-edge technology further into the marketplace and have fun doing it.
Our technology allows consumers to submit a photo or scan an image using their mobile device and Slyce will recognize what it contains and match it to products sold by retailers that have integrated our technology. The focus of our approach is to take our core services and white label our technology into retailers’ apps and mobile web. We also drive experiences with our core consumer apps, including SnipSnap, which reaches more than 5 million monthly users.
We are seeking solid high-level candidates to fill a technical position on our R&D team focused on tasks in the areas of large scale content-based image retrieval, object detection and classification, image segmentation, and more. Successful candidates should have proven experience in these areas and possess a genuine interest in pushing current techniques forward. This role is an exciting opportunity to join a newly formed team and ultimately contribute to its future growth.
- Contribute to commercial R&D projects by developing a variety of algorithms and systems for Slyce’s core search, image and video processing services
- Continuously improve the accuracy and performance of all ML components
- Develop efficient large scale training systems and procedures to facilitate the activities conducted by the team.
- Comparing internal methods to benchmark datasets
- Advance the state-of-the-art in the field, including generating novel contributions and publications in journals and conferences
- 3+ years industry experience working with state-of-the-art CBIR, object detection, and/or image classification techniques
- Experiencing with metric learning
- A solid understanding of feature embeddings and loss functions
- Familiarity with generative techniques
- Familiarity with meta-learning
- Familiarity with graph networks
- Experience with Google's Tensorflow (>= 1.2) is required
- Experience developing methods that leverage multiple GPUs and/or servers
- A solid understanding of experimental designs, data augmentation methods, and validation processes
- Strong hands-on coding experience with Python and/or C++ essential
- Solid grounding in statistical machine learning techniques.
- Must be a fast learner; you will be expected to stay current with what is happening in the field.
- Flexibility and adaptability to work in a growing, dynamic team.
- Acceptance of agile processes and the ability to deliver incremental components
Additional Desirable Qualities:
- Strong experience with OpenCV, NumPy, and SciPy
- Experience designing for distributed processing systems such as Apache Beam, Apache Spark or Hadoop
- Experience using Docker.
- Experience in modern real-time object detection methods
- Experience with model compression and quantization
- Experience using deep learning in a mobile or embedded context