Design and implement ML pipelines that encode visual inputs (pose, face, objects) into shared embeddings. Build and fine-tune CNN and transformer models, perform training, evaluation, augmentation, and hyperparameter tuning. Apply similarity metrics and clustering for behavioral inference and action prediction. Collaborate with cross-functional engineers and produce documented, version-controlled code and model artifacts.
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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