AI / Machine Learning Engineer

Role overview

Responsibilities

• check_circle Design and implement machine learning pipelines that encode visual input (pose, face, object/classification) into shared embedding spaces for similarity and predictive tasks
• check_circle Build and fine-tune convolutional and transformer-based neural architectures optimized for visual recognition and representation learning
• check_circle Develop encoding and embedding techniques that allow consistent comparison across multiple data types (e.g., pose vectors, facial landmarks, class labels)
• check_circle Apply techniques such as cosine similarity, distance metrics, and latent clustering to perform behavioral inference and action prediction
• check_circle Contribute to model training, evaluation, and deployment workflows including data preprocessing, augmentation, hyperparameter tuning, and performance profiling
• check_circle Collaborate closely with engineers in computer vision, embedded systems, software, and UI/UX to ensure seamless integration of AI pipelines into real-time systems
• check_circle Produce clean, well-documented code and maintain version-controlled model artifacts and experiment logs
• check_circle Write technical documentation for models, training procedures, evaluation criteria, and system integration
• check_circle Bachelor’s or Master’s degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, or a closely related discipline
• check_circle 2–3 years of experience in machine learning roles through internships, academic labs, or early career positions
• check_circle Strong understanding of:
• check_circle Convolutional Neural Networks (CNNs) for image and video-based tasks
• check_circle Transformer architectures and their applications in vision or multimodal learning
• check_circle Embedding systems and vector space modeling for semantic and similarity-based tasks
• check_circle Encoding mechanisms and dimensionality reduction techniques for latent representation
• check_circle Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
• check_circle Familiarity with pose estimation, facial recognition, or classification models (e.g., OpenPose, MediaPipe, FaceNet, ResNet variants)
• check_circle Experience training models with structured and unstructured visual datasets
• check_circle Exposure to techniques like cosine similarity, triplet loss, contrastive learning, or temporal prediction modeling
• check_circle Strong computer science fundamentals, including data structures, algorithms, and software design patterns
• check_circle Comfort working in Linux-based development environments and version control systems (Git)
• check_circle A collaborative mindset, with excellent communication skills and a willingness to learn across domains
• check_circle Experience integrating vision-based AI models into embedded or robotics systems
• check_circle Familiarity with ONNX or TensorRT for model optimization and deployment
• check_circle Background in sequence modeling, recurrent architectures, or video-based action recognition
• check_circle Exposure to multimodal AI systems that blend image, pose, and metadata representations
• check_circle Familiarity with techniques like CLIP, DINO, or self-supervised representation learning
• check_circle Experience with MLOps or training orchestration tools such as MLflow, Weights & Biases, or DVC

Benefits

• check_circle Full Health Coverage
• check_circle A collaborative and intellectually driven team environment
• check_circle Flexible PTO
• check_circle We are currently  not accepting applications from third-party recruiting services
• check_circle We are not offering visa sponsorship  for this role at this time. Applicants must be  U.S. citizens or permanent residents (Green Card holders)
• check_circle Candidates must reside within a commutable distance of Santa Monica, California.