Machine Learning Engineer

Escalon Services, LLC.

$100K — $120K *
Enterprise Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's or Master's degree in AI, Data Science, Computer Science, Machine Learning, or related field.
  • 2-3 years of experience in machine learning through internships or early roles.
  • Strong knowledge of Convolutional Neural Networks (CNNs) for visual tasks.
  • Understanding of transformer architectures in vision and multimodal learning.
  • Familiarity with embedding systems for semantic tasks.
  • Proficient in Python and deep learning frameworks like PyTorch or TensorFlow.

Responsibilities

  • Design machine learning pipelines for visual input encoding and predictive tasks.
  • Build and optimize convolutional and transformer-based neural architectures for image recognition.
  • Develop techniques for consistent data type comparison and embedding.
  • Apply distance metrics and clustering for behavioral inference and action prediction.
  • Contribute to model training and deployment workflows, focusing on data preparation and profiling.
  • Collaborate with various engineering teams for smooth integration of AI systems.
  • Produce clean code and maintain detailed documentation for models and processes.

Benefits

  • Comprehensive health coverage and flexible PTO.
  • Opportunity to work on innovative AI, robotics, XR, and autonomous technologies.
  • Collaborative multidisciplinary engineering environment.
  • Career growth and professional development opportunities.
Full Job Description
Machine Learning Engineer

Application Deadline: 30 September 2026

Department: Recruiting Done

Employment Type: Full Time

Location: Santa Monica

Compensation: $100,000 - $120,000 / year

Description

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.

Key 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.


Skills, Knowledge and Expertise
  • 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.

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 techniques like CLIP, DINO, or self-supervised representation learning.
  • Experience with MLOps or training orchestration tools such as 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 at this time.
  • Candidates must reside within a commutable distance of Santa Monica, California.


Benefits
  • Compensation: $100,000 to $120,000 per year
  • Comprehensive health coverage and flexible PTO
  • Opportunity to work on innovative AI, robotics, XR, and autonomous technologies
  • Collaborative multidisciplinary engineering environment
  • Career growth and professional development opportunities

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