Machine Learning Engineer at Escalon
Overview
Escalon seeks a Machine Learning Engineer to design intelligent systems extracting predictive value from computer vision and behavioral datasets. This junior role supports impact from concept to deployment. The position offers a competitive salary in Los Angeles.
What You'll Do8
- 1Design and implement machine learning pipelines that encode visual input into shared embedding spaces for similarity and predictive tasks
- 2Build and fine-tune convolutional and transformer-based neural architectures optimized for visual recognition and representation learning
- 3Develop encoding and embedding techniques enabling consistent comparison across pose vectors facial landmarks and class labels
- 4Apply cosine similarity distance metrics and latent clustering to perform behavioral inference and action prediction
- 5Contribute to model training evaluation and deployment workflows including data preprocessing augmentation and hyperparameter tuning
- 6Collaborate with computer vision embedded systems and UI/UX engineers to integrate AI pipelines into real-time systems
- 7Produce clean documented code and maintain version-controlled model artifacts and experiment logs
- 8Write technical documentation for models training procedures evaluation criteria and system integration
Requirements17
- 1Bachelor's or Master's degree in Artificial Intelligence Data Science Computer Science Machine Learning or related field
- 22–3 years of machine learning experience through internships academic labs or early career positions
- 3Strong understanding of Convolutional Neural Networks for image and video tasks
- 4Deep knowledge of transformer architectures and their applications in vision or multimodal learning
- 5Expertise in embedding systems and vector space modeling for semantic and similarity-based tasks
- 6Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- 7Familiarity with pose estimation facial recognition or classification models like OpenPose MediaPipe FaceNet ResNet variants
- 8Experience training models with structured and unstructured visual datasets
- 9Knowledge of cosine similarity triplet loss contrastive learning or temporal prediction modeling
- 10Solid computer science fundamentals including data structures algorithms and software design patterns
- 11Comfortable working in Linux-based development environments and version control systems
- 12Bonus experience integrating vision-based AI models into embedded or robotics systems
- 13Familiarity with ONNX or TensorRT for model optimization and deployment
- 14Background in sequence modeling recurrent architectures or video-based action recognition
- 15Exposure to multimodal AI systems blending image pose and metadata representations
- 16Familiarity with techniques like CLIP DINO or self-supervised representation learning
- 17Experience with MLOps or training orchestration tools such as MLflow Weights & Biases or DVC
Salary Insight
$100 - $120k per year
Location
Required Skills
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