AI Research Engineer Computer Vision & Analytics

We are seeking a talented and driven AI Engineer to join our innovative team. This role is central to our computer vision and analytics product development, focusing on the end-to-end lifecycle. The successful candidate will be responsible for training, fine-tuning, and rigorously optimizing custom models, like YOLO, to achieve superior accuracy and real-time performance on resource-constrained NVIDIA Jetson edge devices and cloud deployment. Key Responsibilities

Model Training and Development: Lead the complete training pipeline for computer vision based analytical models, including data preparation, augmentation, and implementing custom training and fine-tuning routines to meet specific project goals.

Custom Model Adaptation: Develop and customize model architectures to accurately identify unique and specific needs required by our applications.

Accuracy and Performance Enhancement: Continuously iterate on and experiment with models to systematically improve key metrics such as precision, recall, and mAP (mean Average Precision).

Performance & Cost Optimization: Continuously analyze and optimize cloud resource consumption (e.g., CPU, GPU, memory, and storage) to ensure cost-effective, high-throughput operations and meet budget targets.

Accelerator Implementation: Identify, benchmark, and leverage the most appropriate cloud hardware and software accelerators (e.g., specific GPU/TPU instances, inference compilers like NVIDIA TensorRT, AWS Inferentia) to enhance processing speed and reduce latency.

Deployment and Integration: Work closely with software and hardware engineering teams to ensure the seamless integration and deployment of optimized models into our final products.

Performance Benchmarking: Establish and execute robust testing protocols to measure and report on key performance indicators (KPIs), including inference latency, throughput, accuracy, and cost-per-inference/cost-per-query.

Algorithm Development: Develop analytical algorithms for a variety of use cases, with an emphasis on deep mathematical and theoretical principles.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Electrical Engineering, Artificial Intelligence, or a related technical field.

Strong programming skills in Python.

Proven experience with modern deep learning frameworks such as PyTorch (preferred) or TensorFlow.

Solid foundation in computer vision principles and deep learning concepts.

Hands-on experience with training and fine-tuning machine learning models.

Prior contributions to research papers or projects.

Preferable Skills and Experience - Machine Learning

Direct, hands-on experience with the NVIDIA Jetson family of devices (e.g., Orin, Xavier, Nano) and its development environment.

Demonstrated expertise in model optimization and acceleration using NVIDIA TensorRT.

In-depth knowledge of the YOLO architecture (e.g., YOLOv5, YOLOv8, YOLO-NAS) and experience in custom training it.

Proven experience with model pruning tools and techniques.

Familiarity with MLOps principles and tools (e.g., Docker, MLflow) for managing the machine learning lifecycle.

Experience with video pipeline tools like GStreamer or NVIDIA DeepStream.

A portfolio of relevant projects, a GitHub profile showcasing your work, or contributions to open-source projects.

Preferable Skills and Experience - Mathematical

Strong foundation in calculus, including:

Differentiation (e.g., gradients, Jacobians, Hessians) for optimization in machine learning.

Integration (e.g., continuous probability distributions, loss functions).

Linear algebra (e.g., matrix operations, eigenvalues, singular value decomposition).

Probability and statistics (e.g., Bayesian inference, statistical modeling).

Optimization techniques (e.g., gradient descent, convex optimization).

Soft Skills

Strong problem-solving abilities and attention to detail.

Excellent communication skills for documentation and collaboration.

Ability to work effectively in a team-oriented environment.

What We Offer

Exceptional Peer Environment: The opportunity to work directly with and learn from experts in the field of AI.

Impactful Work: Contribute to fundamental research and have a lasting impact on the industry.

State-of-the-Art Resources: Access to datasets and extensive computational resources to bring your most ambitious ideas to life.

Culture of Growth: A dynamic and intellectually stimulating environment that encourages continuous learning and professional development.