Machine Learning Engineer

Escalon is seeking a Machine Learning Engineer in Santa Monica, CA to design and implement intelligent systems that extract predictive and semantic value from computer vision and behavioral datasets. The role centers on building models that learn shared representations from visual inputs, enabling tasks such as future action prediction, semantic matching, and similarity-based inference.

This is an on-site, full-time position for candidates with hands-on machine learning experience, working closely with engineers across computer vision, embedded systems, software, and UI/UX to integrate AI pipelines into real-time systems.

Key Responsibilities

• Design and implement machine learning pipelines that encode visual inputs such as pose, face, and object/classification signals into shared embedding spaces for similarity and predictive tasks.
• Build and fine-tune convolutional and transformer-based neural architectures for visual recognition and representation learning.
• Develop encoding and embedding techniques that support consistent comparison across multiple data types (including pose vectors, facial landmarks, and class labels).
• Use methods 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 and augmentation, hyperparameter tuning, and performance profiling.
• Collaborate with engineers in computer vision, embedded systems, software, and UI/UX to ensure AI pipelines integrate smoothly into real-time systems.
• Write clean, well-documented code, and maintain version-controlled model artifacts and experiment logs.
• Create technical documentation covering models, training procedures, evaluation criteria, and system integration.

Required Qualifications

• Bachelor’s or Master’s degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, or a closely related field.
• 2 to 3 years of machine learning experience 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, and ResNet variants).
• Experience training models using structured and unstructured visual datasets.
• Exposure to 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 version control systems such as Git.
• Collaborative mindset with excellent 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
• 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 model 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

• Location: Santa Monica, CA (on-site)
• Contract type: Full-time
• Compensation: USD 100,000 to 120,000 per year
• Experience level: 1–2 years (as listed)
• Must be a US Citizen or valid Green Card holder; visa sponsorship is not available.
• Candidates must reside within commutable distance of Santa Monica, California.
• Benefits include comprehensive health coverage and flexible PTO.