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Machine Learning Engineer

Escalon Santa Monica, CA

Santa Monica, CA

Today

$100k - $120k/yr Full time Artificial Intelligence Convolutional Neural Networks Deep Learning

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Job Description

Escalon is seeking a Machine Learning Engineer to design and implement intelligent systems that extract predictive signals and semantic meaning from computer vision and behavioral datasets. The role applies modern deep learning approaches to representation learning, enabling future action prediction, semantic matching, and similarity-based inference in real-world, real-time contexts.

This full-time, on-site position is based in Santa Monica, CA, with compensation listed at USD 100,000 to 120,000 per year. The position calls for 2 to 3 years of experience and 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/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 methods that support 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 to support behavioral inference and action prediction.
Support model training, evaluation, and deployment workflows, including data preprocessing, augmentation, hyperparameter tuning, and performance profiling.
Collaborate with engineers 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 to 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, ResNet variants).
Experience training with 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 Git.
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 model optimization and deployment
Background in sequence modeling, recurrent architectures, or video-based action recognition
Exposure to multimodal AI systems that combine image, pose, and metadata representations
Familiarity with techniques such as CLIP, DINO, or self-supervised representation learning
Experience with MLOps or training orchestration tools including MLflow, Weights & Biases, or DVC

Other Requirements

Must be a US Citizen or a valid Green Card holder. Visa sponsorship is not available for this role.
Candidates must reside within a commutable distance of Santa Monica, California.

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