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Full Time Computer Vision Deep Learning Engineer Jobs

Qualcomm

Computer Vision System Engineer

San Diego, CA · On-site

Engineering Group, Engineering Group > Video Systems, HW Architecture General Summary: Qualcomm ... The team develops next-generation mobile computer vision and deep learning solutions for imaging ...

Norbert Health

Computer Vision/ML Engineer

Brooklyn, NY · On-site

$121K - $142K/yr

... a lead deep learning engineer to spearhead the development of their groundbreaking sensing ... Responsibilities : • Design, fine-tune, and deploy computer vision models (YOLO, InsightFace ...

Hayden AI

Staff Deep Learning Engineer

San Francisco, CA · On-site

$230K - $299K/yr

About Us At Hayden AI, we are on a mission to harness the power of computer vision to transform the ... About the Role As a Staff Deep Learning Engineer in the Deep Learning team at Hayden, you are a ...

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Full Time Computer Vision Deep Learning Engineer information

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$48.5K

$121.5K

$137.5K

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

As of Sep 10, 2026, the average yearly pay for full time 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.

What is the difference between Full Time Computer Vision Deep Learning Engineer vs Machine Learning Engineer?

AspectFull Time Computer Vision Deep Learning EngineerMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with CV and DL frameworksBachelor's or Master's in CS, Data Science, or related; strong programming skills
Work EnvironmentResearch labs, tech companies, AI startups focusing on visual dataTech firms, finance, healthcare, focusing on predictive models
Industry UsagePrimarily in computer vision, robotics, autonomous vehiclesBroader, including NLP, recommendation systems, predictive analytics

The main difference is that Full Time Computer Vision Deep Learning Engineers specialize in visual data analysis using deep learning, while Machine Learning Engineers work on a wider range of predictive models across various data types. Both roles require strong programming skills and knowledge of ML frameworks, but their focus areas and applications differ.

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Full Time Computer Vision Deep Learning Engineer jobs near you
Infographic showing various Full Time Computer Vision Deep Learning Engineer job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 77% Full Time, 16% Part Time, and 6% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $121,515 per year, or $58.4 per hour.

Senior Machine Learning Engineer, Computer Vision/VLM

San Diego, CA • On-site

Waymo
Internet and IT • 1 - 5K employees
Learn more about Waymoexternal

$110K - $152K/yr

Full-time

Re-posted 25 days ago

Key responsibilities

  • Develop and train computer vision and multimodal models to extract semantic information from driving logs.

  • Design and implement a scalable AI agent framework that integrates foundation models with perception outputs and knowledge bases.

  • Own the full system lifecycle, from model development and prototyping to production deployment and scaling.

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 Senior Staff 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.

Apply