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Associate Machine Learning Jobs in Arlington, VA

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site

$103K - $136K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating ... our associates work and provide value to our customers. * Design, develop, test, deploy, and ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site

$103K - $136K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating ... our associates work and provide value to our customers. * Design, develop, test, deploy, and ...

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All JobsAssociate Machine Learning JobsAssociate Machine Learning Jobs in Arlington, VA

Associate Machine Learning information

See Arlington, VA salary details

$36.2K

$153.1K

$361.8K

How much do associate machine learning jobs pay per year?

As of Aug 31, 2026, the average yearly pay for associate machine learning in Arlington, VA is $153,082.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,900.00 and $232,400.00 per year, depending on experience, location, and employer.

What does an associate machine learning engineer do?

An Associate Machine Learning Engineer assists in designing, developing, and deploying machine learning models under the supervision of senior engineers. They handle tasks such as data preprocessing, model evaluation, and maintaining machine learning pipelines. Associates often collaborate with data scientists, software engineers, and business teams to ensure that machine learning solutions are integrated effectively into products or services. This role is typically entry-level or early career and is a stepping stone toward more advanced machine learning positions.

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

To thrive as an Associate Machine Learning Engineer, you need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, usually supported by a relevant degree. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with data processing libraries and version control systems is typically required. Strong analytical thinking, problem-solving ability, and effective collaboration skills help you stand out in this role. These competencies are essential for developing robust models, working efficiently with teams, and delivering impactful data-driven solutions.

What are some common challenges faced by associate machine learning professionals when transitioning from academic projects to real-world business applications?

Associate Machine Learning professionals often find that moving from academic or theoretical projects to business-focused environments introduces new challenges. Real-world datasets can be messy, incomplete, or imbalanced, requiring additional data cleaning and preprocessing. Moreover, business timelines may require rapid prototyping and iterative model development, which is different from the more open-ended nature of academic research. Collaborating with cross-functional teams such as data engineers, product managers, and business stakeholders is also essential to align models with organizational goals. Adapting to these practical aspects is key to succeeding in an Associate Machine Learning role.

What is the difference between Associate Machine Learning vs Data Scientist?

AspectAssociate Machine LearningData Scientist
Required CredentialsBachelor's degree in CS, Data Science, or related field; some roles may require certifications in ML or AIBachelor's or Master's in CS, Statistics, or related; often requires experience with data analysis and programming
Work EnvironmentEntry-level, team-based projects, focused on supporting ML models and data preprocessingMore autonomous, involved in data analysis, model development, and interpretation
Employer & Industry UsageTech companies, startups, research labs; roles in AI and ML teamsWide range of industries including tech, finance, healthcare, and consulting

While both roles involve working with data and machine learning, an Associate Machine Learning typically focuses on supporting ML projects with less experience, whereas a Data Scientist has broader responsibilities including data analysis, model development, and strategic insights. The roles often overlap but differ in scope and experience level.

What are the most commonly searched types of Machine Learning jobs in Arlington, VA?

The most popular types of Machine Learning jobs in Arlington, VA are:

What cities near Arlington, VA are hiring for Associate Machine Learning jobs?

Cities near Arlington, VA with the most Associate Machine Learning job openings:

Associate Machine Learning jobs near you

Research Associate, Atmospheric Science, Machine Learning

DeVine Consulting, Inc.

Silver Spring, MD • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted yesterday

Job description

DeVine provides technical and scientific support to government clients in Oceanography & Atmospheric Science among other technical disciplines.
Our company is looking for a Research Associate, with experience in Atmospheric Science and Machine Learning (ML), to join DeVine in a full time capacity. This position will be supporting a government customer, hence only US Citizens may be considered for hire.  Candidates who meet/exceed every requirement may be considered for remote work.
DeVine contributes to projects in data modeling, remote sensing & machine learning. We collaborate with our clients in scientific analysis of the Earth’s atmosphere & ocean and land surfaces, as well as astronomy and astrometry. We help our clients test and operate space-based, air-based, subsurface, and land and ocean surface-based sensors.
The successful hire will contribute to improvements in weather forecast performance to deliver more accurate weather insights to our customers.
If your experience is relevant to the requirements below, and you’d enjoy working in Silver Spring, MD, then please apply!
Duties:

  • Conduct innovative research at the intersection of weather prediction and machine learning, including approaches that leverage observations from satellite constellation
  • Develop, verify, and document forecast improvements that provide measurable value to customers
  • Partner with engineering and product teams to transition research advances into scalable, operational systems
  • Communicate results through internal reviews, customer discussions, and, where appropriate, conferences or publications
  • Contribute broadly to improving forecasts and overall product performance

Required experience and credentials:

  • Graduate degree in atmospheric science, meteorology, computer science, or a related field
  • 2+ years of experience developing ML models for weather applications
  • Strong ML engineering fundamentals, including model training, validation, evaluation, and documentation
  • Training, running, and verifying AI-based weather prediction models
  • Working in cloud-based computing environments
  • Handling large meteorological datasets and common data formats at scale
  • Modern deep learning frameworks (e.g., PyTorch or TensorFlow)
  • Large geophysical dataset formats (GRIB, NetCDF,  ZARR)
  • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Familiarity with cloud-based computing environments (AWS, GCP, Azure)
  • Strong written and verbal communication skills
  • Ability to manage multiple projects and balance competing priorities
About the position:
  • Position Type: Full-time, Must be U.S. Citizen
  • Location: Silver Spring, MD
  • Benefits: Medical, Dental, Vision, 401K, Life Insurance, Paid Holidays, Paid Sick Leave and Paid Vacation
  • Compensation: $90K to $110K per year salary range DOE and skills

Equal Opportunity Employer
We are committed to a policy of assuring that all applicants for employment are recruited, hired and assigned on the basis of qualifications and merit without discrimination based on any protected classification, including, but not limited to, race, color, religion, sex, sexual orientation, national origin, veteran status, age, disability, handicap, marital status, or any other characteristic protected by applicable laws.

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