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Entry Level Computer Vision Deep Learning Engineer Jobs in Blanco, TX

Avride

Machine Learning Engineer

Austin, TX · On-site

$138K/yr

Real depth in one modern ML area - computer vision, large language models, or generative modelling ... Python and a modern deep learning framework , fluently, as your daily working environment. * Enough ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ... Computer Science, Statistics, Mathematics with equivalent experience. 5+ years of related ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ... Computer Science, Statistics, Mathematics with equivalent experience. 5+ years of related ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ... Computer Science, Statistics, Mathematics with equivalent experience. 5+ years of related ...

Avride

Machine Learning Engineer

Austin, TX · On-site

$138K/yr

Real depth in one modern ML area - computer vision, large language models, or generative modelling ... Python and a modern deep learning framework , fluently, as your daily working environment. * Enough ...

Nvidia Corporation

GPU Compiler Engineer

Austin, TX · On-site

... computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited ... If you're a creative and autonomous engineer with a real passion for technology, we want to hear ...

Nvidia

GPU Compiler Engineer

Austin, TX

... deep-learning compiler technology spanning architecture design and support through functional languages What we need to see: B.S. or degree in Computer Science/Engineering or equivalent experience 2+ ...

Nvidia

GPU Compiler Engineer

Austin, TX · On-site

... computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited ... If you're a creative and autonomous engineer with a real passion for technology, we want to hear ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

About the Role: Engineers on the BCI team utilize signal processing and machine learning to ... Excellent medical, dental, and vision insurance through a PPO plan * Paid holidays * Commuter ...

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All JobsComputer Vision Deep Learning Engineer JobsEntry Level Computer Vision Deep Learning Engineer JobsEntry Level Computer Vision Deep Learning Engineer Jobs in Blanco, TX

Entry Level Computer Vision Deep Learning Engineer information

See Blanco, TX salary details

$44.1K

$110.5K

$125K

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

As of Sep 10, 2026, the average yearly pay for entry level computer vision deep learning engineer in Blanco, TX is $110,510.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,400.00 and $119,600.00 per year, depending on experience, location, and employer.

What does an entry level computer vision deep learning engineer do?

An Entry Level Computer Vision Deep Learning Engineer works on developing and implementing algorithms that allow computers to interpret and understand visual information from the world, such as images or videos. They typically use deep learning techniques, especially neural networks, to build models for tasks like object detection, facial recognition, and image classification. Their responsibilities may include data preprocessing, model training and evaluation, writing code (often in Python), and collaborating with senior engineers on real-world projects. This role is ideal for those who have a strong foundation in machine learning, programming, and mathematics, but are just starting their careers in the field.

What types of projects do entry level computer vision deep learning engineers typically work on, and how is their work structured within a team?

As an entry-level Computer Vision Deep Learning Engineer, you can expect to contribute to projects like object detection, image classification, and model optimization for real-world applications. Your tasks may include data preprocessing, training and evaluating neural networks, and writing code to integrate models into products or pipelines. You'll often collaborate closely with senior engineers, data scientists, and product managers, typically working in agile teams where regular code reviews and knowledge sharing are common. This collaborative environment not only helps you learn best practices but also provides opportunities to gradually take on more responsibility as your skills develop.

What are the key skills and qualifications needed to thrive as an entry level computer vision deep learning engineer, and why are they important?

To thrive as an Entry Level Computer Vision Deep Learning Engineer, you need a solid understanding of computer vision fundamentals, deep learning concepts, and programming skills in languages like Python, along with a relevant degree in computer science, engineering, or a related field. Familiarity with frameworks such as TensorFlow or PyTorch, experience with OpenCV, and knowledge of version control systems like Git are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you collaborate within teams and tackle complex challenges. These skills and qualities are crucial for developing, deploying, and optimizing computer vision solutions that meet real-world business needs.

What cities near Blanco, TX are hiring for Entry Level Computer Vision Deep Learning Engineer jobs?

Cities near Blanco, TX with the most Entry Level Computer Vision Deep Learning Engineer job openings:

Entry Level Computer Vision Deep Learning Engineer jobs near you

Machine Learning Engineer

Austin, TX • On-site

Avride
See more jobs at Avrideexternal

$138K/yr

Full-time

Re-posted 19 days ago

Job description

About the role

We're hiring an experienced ML engineer to work on the models that see. You'll own problems end to end: deciding what data you need, getting it, training on it, proving the result is actually better, and getting it running inside the vehicle's constraints.

The problems you'd be working on

Rather than a list of responsibilities, here's what the team is actually chewing on:

A model that's two points better offline can be worse on the road. Aggregate benchmark numbers hide the failures that matter - the rare scene, the unusual agent, the bad lighting. Building evaluation that predicts on-road behaviour, and knowing when to distrust your own metric, is a bigger part of this job than architecture search.

We generate far more data than anyone can look at. The interesting frames are a vanishingly small fraction of what the fleet records. Finding them, deciding what's worth labelling, and keeping the training set honest as the distribution shifts is continuous work, not a one-time setup.

The vehicle's compute budget is fixed and already full. Everything you add competes with everything already running. You'll be making concrete trades between accuracy, latency, and memory, and defending them.

Modern architectures keep changing what's possible. Transformers and multimodal models opened up approaches that weren't available two years ago. Part of the job is reading what's coming out, judging honestly whether it applies to our problem, and being willing to conclude that it doesn't.

Nothing ships alone. Your model's output is someone else's input. You'll work directly with the planning, infrastructure, and vehicle software teams, and the handoffs are where most of the real difficulty lives.

What we're looking for
  • You've shipped a neural network, not just trained one. At least three years taking models from data collection through training to something that ran in production or on real hardware, and stayed working.
  • Real depth in one modern ML area - computer vision, large language models, or generative modelling. We'd rather see one domain you know properly than six you've touched.
  • Python and a modern deep learning framework, fluently, as your daily working environment.
  • Enough C++ to be useful. Inference runs in C++ on the vehicle. You don't need to be a C++ specialist, but you need to be able to read the code your model runs inside and work with the engineers who own it.
  • Comfort with large-scale data tooling and SQL - you can get your own data without waiting on someone else.
  • You read papers and can tell which ones matter. Most don't.
  • You can explain a technical trade-off to someone who doesn't share your background and hold your position when it's the right call.
Things that would stand out
  • You've made a model meaningfully faster on target hardware and can explain what you gave up to get there.
  • You've worked on ML for autonomous vehicles or robotics before, and know how different the failure modes are from a benchmark.
  • Published work or open-source contributions we can actually read - send us a link and we'll read it.
  • A track record of setting a direction and following it through without needing to be steered.
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