ZipRecruiter

Log In

1

Junior Machine Learning Engineer Jobs in Annapolis, MD

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the next-generation data management and artificial intelligence platform for maritime domain awareness.

Mixpeek

Machine Learning Engineer

Washington, DC · On-site

$200 - $250/hr

The Machine Forward Deployed Learning Engineer position requires a mix of software development, LLM Ops, and SecDevOps practices, resulting in an exciting, fast-paced engineering role. This role ...

previous pagenext page

Showing results 21-40

All JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Annapolis, MD

Junior Machine Learning Engineer information

See Annapolis, MD salary details

$33.2K

$71.1K

$108.4K

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

As of Sep 7, 2026, the average yearly pay for junior machine learning engineer in Annapolis, MD is $71,076.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,000.00 and $79,200.00 per year, depending on experience, location, and employer.

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.

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 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 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.

How much do junior machine learning engineers make?

Junior machine learning engineers typically earn between $70,000 and $100,000 annually, depending on location, education, and industry. Entry-level roles often require knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
More about Junior Machine Learning Engineer jobs

What are the most commonly searched types of Machine Learning Engineer jobs in Annapolis, MD?

The most popular types of Machine Learning Engineer jobs in Annapolis, MD are:

What are popular job titles related to Junior Machine Learning Engineer jobs in Annapolis, MD?

For Junior Machine Learning Engineer jobs in Annapolis, MD, the most frequently searched job titles are:

What cities near Annapolis, MD are hiring for Junior Machine Learning Engineer jobs?

Cities near Annapolis, MD 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 Annapolis, MD as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 25% Part Time, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $71,076 per year, or $34.2 per hour.

Machine Learning Engineer Role

OpenDataJobs

Washington, DC • On-site

Full-time

Re-posted yesterday

Job description

The work
Machine Learning Engineers make machine learning and AI models reproducible, deployable, scalable, and supportable. They build the path from training data and experimentation to a versioned model service that can be released, monitored, retrained, and retired without guesswork.
The role centers on the model lifecycle and the platform beneath it. Machine Learning Engineers automate training and validation, manage features and model artifacts, optimize inference, implement machine learning operations (MLOps), and watch for changes in data, behavior, performance, reliability, and cost. They create the shared tooling that lets data scientists and application engineers move models into production safely.
What you'll build
• Reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts.
• Model-serving systems and APIs designed for appropriate latency, throughput, availability, scaling, and rollback.
• Feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls.
• Monitoring and alerting for data quality, drift, model performance, fairness, infrastructure health, latency, and cost.
• Reusable libraries, templates, environments, and delivery pipelines that give data scientists a tested path from experiment to production.
Who you are
You are comfortable at the seam between modeling and software engineering. You can inspect a model, harden a pipeline, diagnose a production failure, and improve the platform so the same class of problem is easier to prevent next time.
You value repeatability over heroics. You work closely with data scientists on model behavior, data engineers on reliable inputs, AI Engineers on application integration, and platform and security teams on the environment in which the model runs.
What you bring
• Strong programming and software-engineering practice, including testing, version control, packaging, automation, and production debugging.
• Working knowledge of model development, evaluation metrics, feature engineering, data splitting, tuning, and the limits of different modeling approaches.
• Experience with training and inference pipelines, containers, cloud or on-premises compute, artifact management, and automated deployment.
• Practical MLOps experience with model registries, lineage, reproducibility, monitoring, drift analysis, retraining, release controls, and rollback.
• The ability to balance model quality with reliability, interpretability, security, privacy, latency, throughput, and cost.
About OPEN Data Jobs
OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.
Register for Machine Learning Engineer Role
Click Apply below to register for Machine Learning Engineer Role.
Requirements
What openings may require
An opening may emphasize predictive models, computer vision, natural language models, ranking, anomaly detection, recommender systems, edge inference, generative AI model operations, or an enterprise ML platform. Some openings will focus more on model development, while others will focus more on serving and platform engineering.
Specific openings may name Python, SQL, Java, model frameworks, distributed-processing tools, cloud ML services, container orchestration, graphics processing units, feature stores, model registries, experiment tracking, or infrastructure as code. OPEN Data Jobs will identify the required depth for each opening
Benefits
Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening

Apply