Applied Scientist - Computer Vision

Company Description

We are a fast-growing startup dedicated to creating AI-powered products that turn platforms into autonomous systems, allowing users to focus on what truly matters. Our team values collaboration, innovation, and impact, while working at the forefront of cutting-edge AI research. We fuse multimodal LLMs, RAG, reinforcement learning, and edge computer vision into a breakthrough physical AI stack.

Backed by a rapidly expanding base of paying customers and strategic partners, we are seeking an applied computer vision researcher to conduct end-to-end research efforts. This role offers a rare opportunity to shape core technology from the ground up and play a central role in advancing Physical AI.

Role Description

As an AI Researcher at our stealth‑mode startup, you will spearhead the creation of cutting‑edge algorithms in edge AI computer vision. Your work will span the entire lifecycle, from initial exploratory research to practical implementation, as you develop AI solutions that ensure exceptional performance, efficiency, and reliability for edge devices functioning in real-world environments.

Partnering with fellow researchers, you’ll craft novel approaches and innovative training strategies that thrive under tight computational budgets. At the same time, you’ll collaborate with our software engineering team to deliver production-ready solutions that meet rigorous standards for quality, stability, and throughput, while minimizing model footprint and inference latency.

Qualifications

• MSc or PhD in Mathematics, Computer Science, Electrical Engineering, Physics, or a related technical field
• 6+ years of hands-on experience in Python, including libraries such as

PyTorch, OpenCV, etc.

• At least 4 years of experience related to deep learning models for

computer vision tasks, such as object detection, object tracking, object classification, few shot classification, zero shot classification, object identification, semantic segmentation, video based navigation, etc.

• In-depth understanding of machine learning fundamentals, with hands-on experience in training and fine-tuning neural networks.
• Appreciation for best engineering practices, including version control, clean code, code reviews, testing, CI/CD, and automation.
• Strong self-learning and research capabilities - Analyzing models,

research methodologies, optimization methods.

• Team player with strong communication and collaboration skills.

Advantages

• Experience building end-to-end DL pipelines.
• Experience working with MLOps solutions.
• Experience in deploying computer vision solution to UAVs, UGVs, USVs, etc
• Experience in optimization methods of deep learning models.
• Experience in low-voltage chipsets.
• Experience related to LLMs and AI agents.
• Publications in Top-Tier scientific venues.