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Entry Level Machine Learning Engineer Jobs in Austin, TX

Avride

Machine Learning Engineer

Austin, TX · On-site

$138K/yr

About the role We're hiring an experienced ML engineer to work on the models that see. You'll own ... Python and a modern deep learning framework , fluently, as your daily working environment. * Enough ...

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Entry Level Machine Learning Engineer information

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$29.7K

$68.8K

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How much do entry level machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for entry level machine learning engineer in Austin, TX is $68,752.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,000.00 and $77,800.00 per year, depending on experience, location, and employer.

What is an entry level machine learning engineer?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What are some typical projects or tasks an entry level machine learning engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are the most commonly searched types of Machine Learning Engineer jobs in Austin, TX?

The most popular types of Machine Learning Engineer jobs in Austin, TX are:

What are popular job titles related to Entry Level Machine Learning Engineer jobs in Austin, TX?

For Entry Level Machine Learning Engineer jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Entry Level Machine Learning Engineer jobs in Austin, TX look for?

The top searched job categories for Entry Level Machine Learning Engineer jobs in Austin, TX are:

What cities near Austin, TX are hiring for Entry Level Machine Learning Engineer jobs?

Cities near Austin, TX with the most Entry Level Machine Learning Engineer job openings:

Entry Level Machine Learning Engineer jobs near you
Infographic showing various Entry Level Machine Learning Engineer job openings in Austin, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $68,752 per year, or $33.1 per hour.

Machine Learning Engineer

Avride

Austin, TX • On-site

$138K/yr

Full-time

Re-posted 26 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.

#LI-MS1
Candidates are required to be authorized to work in the U.S. The employer is not offering relocation, sponsorship, and remote work options are not available.
Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities. Avride complies with the Americans with Disabilities Act (ADA), if you need a reasonable accommodation to assist with the application or hiring process, or to perform the essential functions of a job, please email jobs@avride.ai.

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