NYC - ON SITE
As our ML Engineer you will be working on some of the most exciting problems in generative image making. You will play a crucial role and will be working with a talented fast-moving team. As a founding member of the team, you will play an important role in setting the direction for the product.
- Lead the development of new generative imaging techniques that can be brought to production in the app being developed utilizing both cloud infrastructure and on-device capabilities of Apple Neural Engine.
- Guide the team towards new opportunities and capabilities from the research field which can enable new features in the app
- Evaluate cost effectiveness of techniques
SKILLS YOU’LL NEED TO BRING
- Machine Learning Expertise: Extensive interest and experience in developing and deploying and training machine learning models, particularly in the field of generative image creation and manipulation. Proficiency in frameworks such as TensorFlow, PyTorch, or similar is required.
- Strong understanding and hands-on experience with deep learning architectures, including Diffusion models, GANs, VAEs, and convolutional neural networks (CNNs). Familiarity with related techniques like style transfer, image synthesis, and image-to-image translation and finetuning is recommended.
- Experience working with ML hosting environments such as HuggingFace, Replicate, RunPod.io or VertexAI, and can set up new environments based on example code.
- Solid understanding of computer vision and image processing techniques, including image segmentation, object detection, and feature extraction. Knowledge of relevant libraries or frameworks like OpenCV is a plus.
- Good communication and problem-solving skills, you will work with a small cross disciplinary team where being able to communicate opportunities to everyone is a must.
NICE TO HAVES
- Research Contributions: Track record of research publications, conference presentations, or contributions to the machine learning community in the field of generative image creation or related areas.
- Data Augmentation: Familiarity with data augmentation techniques for improving model performance and training robustness in generative tasks.
- Experience with deploying ML models at scale and ML ops infrastructure.
- Great open source contributions
HOW TO APPLY
Please send us your resume and any of the following that apply: GitHub profile, Dribbble account, Twitter handle, blog URL, etc. (Anything that helps us learn more about what makes you tick.) Most important, send us a note telling us about what work you’re most proud of, and excited about, and what you’d like to accomplish next.
Be sure to include: your expected salary range, reference contacts, and your earliest possible start date.
Send everything to firstname.lastname@example.org.