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Entry Level Machine Learning Engineer Jobs in New Mexico

Booz Allen Hamilton

Data Engineer

Albuquerque, NM

$111K - $133.30K/yr

Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

Booz Allen Hamilton, Inc.

Data Engineer

Albuquerque, NM · On-site

$111K - $133.30K/yr

Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

Booz Allen Hamilton, Inc.

Data Engineer

Albuquerque, NM · On-site

$111K - $133.30K/yr

Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

Booz Allen Hamilton

Data Engineer

Albuquerque, NM

$111K - $133.30K/yr

Ever-expanding technology like IoT, machine learning, and artifi cia l intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

Varsity Tutors

Data Science Tutor

Albuquerque, NM · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

University of New Mexico

HS Research Tech 2

Albuquerque, NM

$17.50 - $24/hr

... programming skills in Python, R, and/or MATLAB, with the ability to apply computational approaches to research questions. Experience or familiarity with artificial intelligence and machine learning ...

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Entry Level Machine Learning Engineer information

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$114.3K

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

As of Jun 1, 2026, the average yearly pay for entry level machine learning engineer in New Mexico is $67,217.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,900.00 and $76,100.00 per year, depending on experience, location, and employer.

What is an Entry Level Machine Learning Engineer job?

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 the key skills and qualifications needed to thrive in the Entry Level Machine Learning Engineer position, and why are they important?

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 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.
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Entry Level Machine Learning Engineer jobs near you
Artificial Intelligence for Materials Postdoctoral Research Associate

Artificial Intelligence for Materials Postdoctoral Research Associate

Los Alamos National Laboratory

Los Alamos, NM • On-site

Full-time

Posted 8 hours ago

Los Alamos National Laboratory rating

9.2

Company rating: 9.2 out of 10

Based on 32 frontline employees who took The Breakroom Quiz

7th of 103 rated laboratories

Job description

Job Summary:
Los Alamos National Laboratory is a multidisciplinary research institution engaged in strategic science on behalf of national security. They are seeking a highly motivated post-doctoral candidate in the areas of machine learning and AI for materials science, focusing on establishing structure-property relationships from materials datasets. The role involves integrating with project teams and conducting original research with the aim of publishing findings in peer-reviewed journals.
Responsibilities:
• Demonstrated expertise in one or more of the following: Machine learning for materials data, including regression and classification on materials and microstructure datasets.
• Materials modeling experience relevant to microstructure and mechanics (e.g., MD/DFT, phase-field, crystal plasticity, microstructure-property relationships).
• Uncertainty quantification (UQ) and model reliability, including approaches such as calibrated probabilistic prediction, Bayesian/ensemble methods.
• Development and use of atomic-scale descriptors for learning from atomistic datasets (SFD, ACE, MTP, SNAP), including feature construction, and integration into ML workflows.
• Strong programming skills in Python (e.g., NumPy/SciPy, pandas,) and ML frameworks such as scikit-learn, PyTorch, JAX, and TensorFlow.
• Demonstrated experience in conducting original scientific research through peer reviewed publication record.
• Excellent communication skills (both oral and written).
Qualifications:
Required:
• Demonstrated expertise in one or more of the following: Machine learning for materials data, including regression and classification on materials and microstructure datasets.
• Materials modeling experience relevant to microstructure and mechanics (e.g., MD/DFT, phase-field, crystal plasticity, microstructure-property relationships).
• Uncertainty quantification (UQ) and model reliability, including approaches such as calibrated probabilistic prediction, Bayesian/ensemble methods.
• Development and use of atomic-scale descriptors for learning from atomistic datasets (SFD, ACE, MTP, SNAP), including feature construction, and integration into ML workflows.
• Strong programming skills in Python (e.g., NumPy/SciPy, pandas,) and ML frameworks such as scikit-learn, PyTorch, JAX, and TensorFlow.
• Demonstrated experience in conducting original scientific research through peer reviewed publication record.
• Excellent communication skills (both oral and written).
• A STEM PhD in areas such as Materials Science, Computational Physics, Engineering, or related fields, completed within the last five years or soon to be completed.
Preferred:
• Solid Background in materials science and engineering.
• Experience training ML/AI models at scale on GPU-accelerated HPC systems, including managing large datasets/workloads and performance-aware workflows.
• Ability to adapt to new requirements for projects and be flexible enough to learn new areas of research as needed.
• Ability to work effectively as a part of a team in a multi-disciplinary environment and interact with people with a variety of expertise.
Company:
Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is Founded in 1943, the company is headquartered in Los Alamos, USA, with a team of 10001+ employees. The company is currently Late Stage.

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