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Contract Computer Vision Deep Learning Engineer Jobs

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Senior Deep Learning Engineer

Manhattan, NY

$115.20K - $158.20K/yr

We're looking for a Senior Deep Learning Engineer with extensive experience in modern neural network techniques and PyTorch to help us push the boundaries of computer vision in real-world ...

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How much do contract computer vision deep learning engineer jobs pay per year?

As of Jun 3, 2026, the average yearly pay for contract computer vision deep learning engineer in the United States is $121,515.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $131,500.00 per year, depending on experience, location, and employer.
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Contract Computer Vision Deep Learning Engineer jobs near you
Infographic showing various Contract Computer Vision Deep Learning Engineer job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 88% Full Time, 6% Part Time, and 5% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $121,515 per year, or $58.4 per hour.
Senior Machine Learning Engineer, Computer Vision/VLM

Senior Machine Learning Engineer, Computer Vision/VLM

Waymo

San Diego, CA

$110.90K - $152.40K/yr

Other

Posted 16 days ago

Job description

In Semantics, our team's mission is to create the highest-fidelity, most comprehensive offboard perception autolabels at a massive scale, serving as the foundation for training and validating the AV stack. We are an advanced ML and engineering team that leverages state-of-the-art computer vision, deep learning, and generative AI to automatically analyze driving logs, generate rich scene understanding, and power the data engine that enables Waymo to scale safely and efficiently.

In this hybrid role, you will report to a Technical Lead Manager.

You will:

  • Develop and train state-of-the-art computer vision / multimodal models (e.g., Gemini) to extract the rich semantic information (e.g., object attributes, scene properties, interaction dynamics) required by the AI agent.
  • Design and implement a scalable AI agent framework that integrates large foundation models (e.g., Gemini) with the outputs of our perception models and internal knowledge bases.
  • Develop and apply Fine-tuning and Reinforcement Learning (RL) techniques to create a "data flywheel," continuously improving the system's captioning and reasoning abilities through automated feedback.
  • Develop and prototype novel prompting strategies for Vision-Language Models (VLMs) to elicit complex, causal reasoning about driving scenarios.
  • Collaborate closely with the ML Infra, Perception, Behavior, and AI Foundation teams to define data requirements and integrate the captioning system into the broader ML development lifecycle.
  • Own the full system lifecycle, from advanced model development and prototyping to production deployment and scaling for massive data generation

You have:

  • Master's degree in Computer Science, or a related technical field.
  • 4+ years of hands-on experience training and shipping deep learning models for computer vision tasks (e.g., detection, segmentation, video understanding) using Python and frameworks like PyTorch, JAX, or TensorFlow.
  • 1+ years of demonstrated experience working with large language models (LLMs) or vision-language models (VLMs) in areas such as fine-tuning, prompting, or Retrieval-Augmented Generation (RAG).
  • Strong software engineering fundamentals, including designing scalable and reliable systems.
  • Experience building and managing large-scale data processing pipelines for ML training.
  • Proven ability to work autonomously and lead complex technical projects in a fast-paced R&D environment.

We prefer:

  • PhD in Computer Science, or a related technical field.
  • Publication record in top-tier AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR).
  • Hands-on experience with Reinforcement Learning, especially RLHF, RLAIF, or applying RL to language/agentic tasks.
  • Experience with modern techniques in self-supervised, weakly-supervised, or multi-task learning for perception.
  • Experience building with AI agent frameworks (e.g., LangChain, LlamaIndex) or developing autonomous agentic systems.
  • Familiarity with the challenges of multimodal perception in robotics or autonomous driving.
  • A track record of impactful cross-functional collaboration.

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