Machine Learning Engineer

Escalon is seeking a Machine Learning Engineer to help design and implement intelligent systems that extract meaning and predictive value from computer vision and behavioral datasets. The work centers on building models and embedding architectures that support future action prediction, semantic matching, and similarity-based inference, with training and deployment workflows built for real-world use.

This full-time, on-site role is based in Santa Monica, CA, with compensation of $100,000 to $120,000 per year. The position is intended for candidates with roughly 2–3 years of experience through internships, academic labs, or early career roles, and requires a Bachelor’s or Master’s degree in a relevant field.

Responsibilities

• Design and implement machine learning pipelines that encode visual inputs (such as pose, face, and object or classification signals) into shared embedding spaces used for similarity and predictive tasks.
• Build and fine-tune convolutional and transformer-based neural architectures focused on visual recognition and representation learning.
• Develop encoding and embedding methods that enable consistent comparison across multiple data types, including pose vectors, facial landmarks, and class labels.
• Use techniques such as cosine similarity, distance metrics, and latent clustering for behavioral inference and action prediction.
• Support model training, evaluation, and deployment workflows, including data preprocessing, augmentation, hyperparameter tuning, and performance profiling.
• Partner with teams across computer vision, embedded systems, software, and UI/UX to integrate AI pipelines into real-time systems.
• Produce clean, well-documented code and maintain version-controlled model artifacts and experiment logs.
• Write technical documentation covering models, training procedures, evaluation criteria, and system integration.

Requirements

• 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 for 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 for latent representations.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with pose estimation, facial recognition, or classification models (for example: OpenPose, MediaPipe, FaceNet, or ResNet variants).
• Experience training models using structured and unstructured visual datasets.
• Exposure to techniques such as 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 using Git for version control.
• A collaborative mindset, with strong communication skills and willingness to learn across domains.

Technologies

• Python, PyTorch, TensorFlow
• Git
• OpenPose, MediaPipe, FaceNet, ResNet
• ONNX, TensorRT
• MLflow, Weights & Biases, DVC
• CLIP, DINO

Benefits

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

Bonus (Nice-to-Have)

• Experience integrating vision-based AI models into embedded or robotics systems
• Familiarity with ONNX or TensorRT for optimization and deployment
• Background in sequence modeling, recurrent architectures, or video-based action recognition
• Exposure to multimodal AI systems that blend image, pose, and metadata representations
• Familiarity with CLIP, DINO, or self-supervised representation learning
• Experience with MLOps or training orchestration tools such as MLflow, Weights & Biases, or DVC

Additional Information

• Work arrangement: On-site
• Contract type: Full-time
• Compensation: $100,000 to $120,000 per year
• Eligibility: Must be a US Citizen or valid Green Card holder; visa sponsorship is not available.
• Location requirement: Candidates must reside within a commutable distance of Santa Monica, California.