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

Varsity Tutors

Machine Learning Tutor

Austin, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Varsity Tutors

Machine Learning Tutor

San Marcos, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Dashiell Corportaion

CAD Technician II

Austin, TX · On-site

Engineering Location: Austin, TX Description Dashiell is a leading national provider of technical ... entry level CAD Technicians * Perform special projects and complete other duties as assigned or ...

Dashiell

CAD Technician II

Austin, TX · On-site

These services include planning and systems studies, design and engineering, maintenance and ... entry level CAD Technicians * Perform special projects and complete other duties as assigned or ...

Allen Control Systems

Software Engineer

Austin, TX · On-site

We are developing a small, autonomous gun turret that employs advanced computer vision and control ... Exposure to machine learning frameworks (TensorFlow, PyTorch) or scientific computing. * Experience ...

Allen Control Systems

Software Engineer

Austin, TX · On-site

We are developing a small, autonomous gun turret that employs advanced computer vision and control ... Exposure to machine learning frameworks (TensorFlow, PyTorch) or scientific computing. * Experience ...

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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 Aug 16, 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 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 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 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

Senior Reinforcement Learning Engineer

Gravis Robotics

Austin, TX

$103K - $142K/yr

Full-time

Posted 4 days ago

Job description

Gravis Robotics is a startup turning heavy construction machines into intelligent and autonomous robots. Our unique combination of learning-based automation and augmented remote control enables a single operator to safely manage a fleet of earthmoving machines in a gamified environment. With over a decade of academic experience at the cutting edge of large-scale robotics, our team is rapidly translating this expertise into real-world deployments with industry leaders in a trillion-dollar market.

Our Rooftop Autonomous Control Kit (RACK) integrates sensing, compute, communication, and networking into a manufacturer-agnostic solution that works across a wide range of construction machines. We operate at the intersection of hardware, software, and real-world deployment, and we're growing fast.

About the Job
 
The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. You will build control modules that run on many different machines, across many sites, with different soil conditions. We’re looking for a roboticist with data driven planning and/or control background, deep python expertise and good level of C++ proficiency.
 
To be successful in this role you should have experience working with real robots, tackling the challenges of sim2real transfer, and deploying robotic systems in a production environment.
 
The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. In this role, you will develop control modules designed to run across diverse machines, sites, and soil conditions. We are looking for a roboticist with a background in data-driven planning and/or control, strong Python skills, and a solid working knowledge of C++.
 
To thrive in this role, you should have experience working with physical robots, navigating the challenges of sim-to-real (sim2real) transfer, and deploying robotic systems into production environments.
What you will do
Learning-Based Planning and Control for Real Systems
 
  • Develop data driven planning and control systems for autonomous excavation that generalize across machine models and soil conditions
  • Contribute to  simulation improvements that reduce or address the sim2real gap
  • Define data collection and curation pipelines for incorporating real data in policy training 
  • Design experiments focused on continuous performance and robustness improvements.
  • Explore the usage of adaptive and online reinforcement learning in deployed systems
  • Provide mentorship and supervision for junior team members, interns, and students.
 
System Integration
 
  • Integrate learned components into a larger software stack
  • Collaborate with excavation and motion planning engineers
  • Build tools for analysing and evaluating the behavior of learned components
What we’re looking for

We recognize that excellent candidates come from diverse backgrounds with various combinations of skills. If you meet most of the core qualifications below, we highly encourage you to apply.

Core qualifications

  • 2–5 years industry experience developing Reinforcement learning systems for control and/or planning and deploying them on real robots with a customer. If you only have experience with simulation, you’re most likely not a good fit for this position.

  • Experience with GPU accelerated simulation environments (e.g. IsaacSim/IsaacLab, CARLA, MuJoCo)

  • Strong Python skills and experience with PyTorch or similar libraries

  • Proficiency in C++

  • Comfortable debugging real-world system behavior

  • Ability and willingness to travel as required by business projects. 

Great-to-Have Skills & Experience

  • Experience with hydraulic machinery

  • Experience with supervised learning or imitation learning

  • Research experience in reinforcement learning

  • Experience deploying robotic systems at scale (e.g. hundreds of units)

  • Familiarity with ROS or similar robotics frameworks

  • Experience with feature-flagged deployments, staged rollouts, or long-lived platforms

  • Experience with data curation for ML applications

  • Experience guiding, mentoring, or leading junior colleagues, students, or project teams. 

  • Familiarity with or interest in utilizing AI coding tools. 

This Role is a Great Fit If

  • You are passionate about building systems that work reliably in the real world

  • You want to help build a long-lived excavation planning and control system intended to scale and positively impact the entire construction industry. 

  • You are comfortable working with the realities of imperfect data and noisy measurements.

  • You have a keen interest in bridging the sim2real gap and understanding the differences between simulation and physical environments. 

  • You are excited to help drive technical direction in a growing team transitioning from prototyping to the product stage.

  • You value a collaborative team culture rooted in thoughtful design, creative thinking, mutual respect, and pragmatism. 

Don't meet every requirement? If you're enthusiastic about this role but your experience doesn't match every qualification, we still encourage you to apply. You might be the perfect candidate for this or other positions. This is an opportunity to join a dynamic and versatile team, and to be part of a young startup that will revolutionize heavy construction.
 
Gravis Robotics offers a fair market salary and a working location in the vibrant city of Zurich. As a forward-facing startup, we understand that work-life balance and flexibility are important considerations for many professionals:
 
If you are a highly qualified candidate with the requisite skills and experience, we encourage you to apply and discuss your preferred working arrangement during the interview process. Gravis is an equal opportunity employer.
 
We are committed to building an inclusive and diverse team, and do not discriminate based upon race, color, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics. We are an international team that is working to solve problems with a global impact: to facilitate efficient communication and collaboration, proficiency in English is a requirement for all roles.
 
 
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.