Senior Machine Learning Engineer

Senior Machine Learning Engineer – Perception R&D Hybrid Contract

C2C

GC, USC

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Python, PyTorch, computer vision, deep learning (ViT/Transformers, self-supervised, vision-language models, zero-shot detection), model distillation/quantization, Docker, cloud (AWS/GCP), experiment tracking, and strong communication of AI concepts.

Job Description

We are seeking a Senior Machine Learning Engineer (Perception R&D) to join an advanced research and development team focused on building state-of-the-art perception systems. This role is ideal for a hands-on engineer with strong experience in computer vision and deep learning , who enjoys solving complex real-world problems and translating research into production-ready solutions.

The ideal candidate will work on modern vision architectures, explore emerging techniques, and collaborate closely with engineering and product teams to deliver high-impact AI-driven capabilities.

Responsibilities

• Design, develop, and optimize deep learning models for perception and computer vision tasks

• Conduct applied research and advanced R&D using modern architectures such as Vision Transformers (ViT), Transformers, vision-language models, and self-supervised learning

• Tackle real-world perception challenges including occlusion, lighting variations, and reflective surfaces

• Evaluate tradeoffs between machine learning models and heuristic-based approaches

• Implement model distillation, quantization, and optimization for efficient deployment

• Train, fine-tune, and evaluate models using PyTorch and Python

• Collaborate with cross-functional teams to integrate models into production pipelines

• Communicate technical concepts, model performance, and tradeoffs clearly to engineering and product stakeholders

• Required Qualifications

• 5+ years of experience in computer vision and deep learning (applied research or advanced R&D)

• Strong proficiency in Python and PyTorch

• Experience with modern deep learning architectures (ViT/Transformers, self-supervised learning, vision-language models, zero-shot detection)

• Proven ability to solve complex perception problems in real-world environments

• Strong problem-solving and analytical skills

Preferred Qualifications

• Experience with model distillation and quantization

• Familiarity with Docker and containerized workflows

• Experience with cloud platforms such as AWS or GCP

• Familiarity with experiment tracking tools (e.g., MLflow, Weights & Biases)

• Background in deploying ML models to production