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Junior Machine Learning Engineer Jobs in Austin, TX

Bespoke Labs

Machine Learning Engineer

Austin, TX

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

We are looking for a passionate, highly motivated, and hands-on applied Senior Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by helping to research and ...

PTP

Machine Learning Engineer - REMOTE

Austin, TX · On-site +1

We are looking for a Machine Learning Engineer to help us design and deliver CX solutions that provide our clients with a beautiful customer journey that achieves results. At PTP we value aptitude ...

Fluidstack

Machine Learning Engineer

Austin, TX · On-site

$130 - $180/hr

Partner with data engineering and product pods to put predictions in the tools people already use. What We're Looking For * The below is a starting point. We always make space for exceptional people ...

Bumble Inc.

Senior Machine Learning Engineer

Austin, TX · On-site

$220K - $250K/yr

As a Senior Machine Learning Engineer, you'll own impactful problems end-to-end-from data exploration through to production deployment, while collaborating closely with Product, Engineering, and Data ...

Bumble Inc.

Senior Machine Learning Engineer

Austin, TX

$220K - $250K/yr

As a Senior Machine Learning Engineer, you'll own impactful problems end-to-end-from data exploration through to production deployment, while collaborating closely with Product, Engineering, and Data ...

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Junior Machine Learning Engineer information

See Austin, TX salary details

$33.2K

$71.2K

$108.5K

How much do junior machine learning engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for junior machine learning engineer in Austin, TX is $71,168.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,100.00 and $79,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a junior machine learning engineer, and why are they important?

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

What kinds of projects and responsibilities can a junior machine learning engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

What is the difference between Junior Machine Learning Engineer vs Data Scientist?

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

What does a junior machine learning engineer do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

More about Junior Machine Learning Engineer jobs
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 Junior Machine Learning Engineer jobs in Austin, TX? For Junior Machine Learning Engineer jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Junior Machine Learning Engineer jobs in Austin, TX look for? The top searched job categories for Junior Machine Learning Engineer jobs in Austin, TX are:
What cities near Austin, TX are hiring for Junior Machine Learning Engineer jobs? Cities near Austin, TX with the most Junior Machine Learning Engineer job openings:
Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 22% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $71,168 per year, or $34.2 per hour.

Machine Learning Engineer

Bespoke Labs

Austin, TX

Full-time

Re-posted 21 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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