Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. * Transform machine learning ...
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Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. * Transform machine learning ...
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Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. * Transform machine learning ...
Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. * Transform machine learning ...
Quick apply
Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. * Transform machine learning ...
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Quick apply
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Junior Machine Learning Engineer information
See Virginia salary details
$33.2K - $40.1K
3% of jobs
$40.1K - $46.9K
20% of jobs
$48.4K is the 25th percentile. Wages below this are outliers.
$46.9K - $53.8K
7% of jobs
$53.8K - $60.6K
6% of jobs
$60.6K - $67.5K
12% of jobs
The median wage is $67.9K / yr.
$67.5K - $74.3K
17% of jobs
$78K is the 75th percentile. Wages above this are outliers.
$74.3K - $81.2K
17% of jobs
$81.2K - $88K
7% of jobs
$88K - $94.9K
4% of jobs
$94.9K - $101.7K
3% of jobs
$101.7K - $108.6K
2% of jobs
$33.2K
$71.2K
$108.6K
How much do junior machine learning engineer jobs pay per year?
What are the key skills and qualifications needed to thrive as a Junior Machine Learning Engineer, and why are they important?
What kinds of projects and responsibilities can a Junior Machine Learning Engineer expect in their first year on the job?
What engineers make $500,000?
How much does a junior ML engineer earn?
What is the difference between Junior Machine Learning Engineer vs Data Scientist?
| Aspect | Junior Machine Learning Engineer | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's in CS, Data Science, or related; some experience with ML frameworks | Bachelor's or higher in CS, Statistics, or related; often advanced certifications |
| Work Environment | Developing and deploying ML models, coding, testing | Data analysis, statistical modeling, interpreting data insights |
| Employer & Industry Usage | Tech companies, startups, AI-focused firms | Finance, healthcare, tech, consulting |
| Search & Comparison Intent | Yes | Yes |
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?
Which 5 jobs will survive AI?
Can I learn ML in 3 months?
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.
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Job description
Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. We're proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission's biggest challenges.
Dark Wolf is seeking a highly motivated and self-directed professional to fill the role of Machine Learning (ML) Engineer to support our team in Northern Virginia.
Responsibilities:
- Design, develop, and implement machine learning models and algorithms to solve specific business problems.
- Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
- Transform machine learning models into deployable APIs and integrate them with existing applications and infrastructure.
- Collaborate closely with data scientists, software engineers, and product managers to understand requirements and translate them into practical ML solutions.
- Experiment with different machine learning techniques and algorithms to identify the most effective approaches for given problems.
- Evaluate model performance using appropriate metrics and iterate on models to improve accuracy, efficiency, and scalability.
- Monitor and maintain deployed models, ensuring their reliability and performance in production environments.
- Troubleshoot and resolve issues related to machine learning models and pipelines.
- Stay up-to-date with the latest advancements in machine learning, deep learning, and related fields.
- Contribute to the development of best practices and standards for machine learning development and deployment within the team.
- Document machine learning models, experiments, and deployment processes.
- Potentially work with large datasets and big data technologies.
- Optimize machine learning models for performance and efficiency.
Qualifications:
- Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science or related fields.
- Demonstrated hands-on experience in developing and deploying machine learning models in a production environment.
- Strong programming skills in Python and experience with relevant machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, scikit-learn, etc.
- Solid understanding of machine learning algorithms (e.g., regression, classification, clusting, dimensionality reduction, deep learning architectures).
- Experience with data preprocessing, feature engineering, and data visualization techniques.
- Familiarity with data storage and processing technologies (e.g., SQL, NoSQL databases, Spark, Hadoop).
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and their machine learning services.
- Understanding of software development principles, version control (e.g., Git), and CI/CD pipelines.
- Strong analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions.
- Excellent communication and collaboration skills to effectively communicate technical concepts to both technical and non-technical audiences.
Preferred Skills:
- Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems.
- Experience with MLOps practices and tools for automating and monitoring machine learning workflows.
- Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.
- Experience with building and deploying RESTful APIs.
- Familiarity with big data technologies and distributed computing.
- Experience with statistical modeling and inference.
Position Clearance Requirement:
TS/SCI with Full-Scope Polygraph
This position is located in Chantilly/Herndon, VA.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
About Dark Wolf Solutions
Sourced by ZipRecruiter
Industry
It services
Company size
51 - 200 Employees
Headquarters location
Chantilly, VA, US
Year founded
2013