Machine Learning Engineer

Escalon Services is a technology company developing next-generation intelligent systems at the intersection of AI, XR, robotics, autonomy, and spatial computing. They are seeking a Machine Learning Engineer to help design and implement intelligent systems that extract meaning and predictive value from computer vision and behavioral datasets.

What you'll do:

• Design and implement machine learning pipelines that encode visual input (pose, face, object/classification) into shared embedding spaces for similarity and predictive tasks
• Build and fine-tune convolutional and transformer-based neural architectures optimized for visual recognition and representation learning
• Develop encoding and embedding techniques that allow consistent comparison across multiple data types (e.g., pose vectors, facial landmarks, class labels)
• Apply techniques such as cosine similarity, distance metrics, and latent clustering to perform behavioral inference and action prediction
• Contribute to model training, evaluation, and deployment workflows including data preprocessing, augmentation, hyperparameter tuning, and performance profiling
• Collaborate closely with engineers in computer vision, embedded systems, software, and UI/UX to ensure seamless integration of AI pipelines into real-time systems
• Produce clean, well-documented code and maintain version-controlled model artifacts and experiment logs
• Write technical documentation for models, training procedures, evaluation criteria, and system integration

What they're looking for:

• Bachelor's or Master's degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, or a closely related discipline
• 2–3 years of experience in machine learning roles through internships, academic labs, or early career positions
• Strong understanding of Convolutional Neural Networks (CNNs) for image and video-based tasks
• Strong understanding of transformer architectures and their applications in vision or multimodal learning
• Strong understanding of embedding systems and vector space modeling for semantic and similarity-based tasks
• Strong understanding of encoding mechanisms and dimensionality reduction techniques for latent representation
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
• Familiarity with pose estimation, facial recognition, or classification models (e.g., OpenPose, MediaPipe, FaceNet, ResNet variants)
• Experience training models with structured and unstructured visual datasets
• Exposure to techniques like cosine similarity, triplet loss, contrastive learning, or temporal prediction modeling
• Strong computer science fundamentals, including data structures, algorithms, and software design patterns
• Comfort working in Linux-based development environments and version control systems (Git)

Benefits:

• Comprehensive health coverage
• Flexible PTO
• Full health coverage.
• Flexible PTO.
• A collaborative and intellectually driven team environment.
• The opportunity to work on cutting-edge AI systems supporting mission-critical applications.