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Entry Level Machine Learning Visa Sponsorship Jobs

Avride

Machine Learning Engineer

Austin, TX · On-site

$138K/yr

Python and a modern deep learning framework , fluently, as your daily working environment. * Enough ... The employer is not offering relocation, sponsorship, and remote work options are not available.

SynergisticIT

AI & Machine Learning Engineer

Seattle, WA · On-site

$130K - $156K/yr

Entry-Level AI Programmer -- Learn to Build Useful AI Applications AI is changing how companies ... Visa, Western Union, Wells Fargo, Client, Paypal, Banking, Wayfair, Client, Client and hundreds ...

AJ Rose

2nd shift Machine Operator

Avon, OH · On-site

$17.07 - $21.66/hr

Kickstart Your Manufacturing Career! Entry-Level Machine Operator Looking to get your foot in the ... Enjoys learning new equipment * Takes pride in quality work Apply today and start building a career ...

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Entry Level Machine Learning Visa Sponsorship information

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How much do entry level machine learning visa sponsorship jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for entry level machine learning visa sponsorship in the United States is $17.46, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $18.99 per hour, depending on experience, location, and employer.

What types of projects and collaboration can an entry level machine learning employee expect, especially when working under visa sponsorship?

Entry level machine learning professionals typically work on well-defined tasks such as data preprocessing, model training, and assisting with algorithm development under the guidance of senior team members. Collaboration is a key part of the role, often involving cross-functional teams including data engineers, software developers, and domain experts. Those on visa sponsorship can expect structured onboarding, mentorship opportunities, and regular feedback to support both technical growth and integration into the team. Many organizations provide clear project scopes and documentation, making it easier for new hires to ramp up and contribute effectively.

What is an entry level machine learning visa sponsorship job?

Entry Level Machine Learning Visa Sponsorship jobs are positions in the field of machine learning that are suitable for candidates with little to no professional experience and are open to applicants who require employer sponsorship for a work visa. These roles typically involve assisting with data analysis, building machine learning models, and supporting senior engineers or scientists. Employers offering visa sponsorship help international candidates legally work in the country, often supporting H-1B or similar visa processes. Such jobs are common in technology companies, research labs, and startups that need fresh talent and are open to hiring globally.

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

To thrive as an Entry Level Machine Learning Engineer, you need a solid background in computer science, statistics, and mathematics, often supported by a relevant degree or coursework. Experience with programming languages like Python or R, familiarity with ML libraries (such as TensorFlow or scikit-learn), and knowledge of data processing tools are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you collaborate and deliver impactful solutions. These skills ensure you can build, evaluate, and deploy machine learning models that address real-world challenges and support business objectives.

What is the difference between Entry Level Machine Learning Visa Sponsorship vs Entry Level Data Scientist Visa Sponsorship?

AspectEntry Level Machine Learning Visa SponsorshipEntry Level Data Scientist Visa Sponsorship
Required CredentialsBachelor's in CS, ML, or related; basic programming skillsBachelor's in CS, Statistics, or related; programming and analytical skills
Work EnvironmentResearch labs, tech companies, startupsTech firms, finance, healthcare, consulting
Industry UsageDeveloping ML models, algorithmsAnalyzing data, building predictive models
Common Search IntentVisa sponsorship for ML rolesVisa sponsorship for data science roles

Both roles require similar educational backgrounds and programming skills, often working in tech-driven environments. The main difference lies in focus: Machine Learning roles emphasize developing algorithms and models, while Data Scientist roles focus on analyzing data and deriving insights. Visa sponsorship processes are comparable, but candidates should tailor their applications to the specific role's requirements.

More about Entry Level Machine Learning Visa Sponsorship jobs
What cities are hiring for Entry Level Machine Learning Visa Sponsorship jobs? Cities with the most Entry Level Machine Learning Visa Sponsorship job openings:
What are the most commonly searched types of Machine Learning Visa Sponsorship jobs? The most popular types of Machine Learning Visa Sponsorship jobs are:
What states have the most Entry Level Machine Learning Visa Sponsorship jobs? States with the most job openings for Entry Level Machine Learning Visa Sponsorship jobs include:
What job categories do people searching Entry Level Machine Learning Visa Sponsorship jobs look for? The top searched job categories for Entry Level Machine Learning Visa Sponsorship jobs are:
Entry Level Machine Learning Visa Sponsorship jobs near you
Infographic showing various Entry Level Machine Learning Visa Sponsorship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $36,327 per year, or $17.5 per hour.

Machine Learning Engineer

Avride

Austin, TX • On-site

$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.

#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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