Machine Learning Engineer

Escalon Services.•Santa Monica, CA

1d•Onsite

About The Position

Our client is a technology company developing next-generation intelligent systems at the intersection of AI, XR, robotics, autonomy, and spatial computing. Their products support mission-critical applications across defense, public safety, and critical infrastructure. They are seeking passionate professionals who thrive in fast-paced environments and enjoy building impactful products from concept to deployment. The Role Our client 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. This is a junior-level, in-person role suited for candidates with 2–3 years of experience and a solid foundation in deep learning, embeddings, and modern neural architectures. As a member of the AI team, the ideal candidate will work on projects that leverage CNNs, transformer models, and embedding architectures to encode and reason over pose, facial, and action-based visual data. These systems support downstream tasks such as future action prediction, semantic matching, and similarity-based inference.

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 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).
A collaborative mindset, with excellent communication skills and a willingness to learn across domains.

Nice To Haves

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 techniques like CLIP, DINO, or self-supervised representation learning.
Experience with MLOps or training orchestration tools such as MLflow, Weights & Biases, or DVC.

Responsibilities

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 behavioural 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 artefacts and experiment logs.
Write technical documentation for models, training procedures, evaluation criteria, and system integration.