Machine Learning Engineer

Description

About Our Client

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.

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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