Machine Learning Engineer
Herndon, VA · On-site
Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. * Transform machine learning ...
Machine Learning Engineer
Herndon, VA · On-site
Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. * Transform machine learning ...
Conduct data analysis and preprocessing to ensure high-quality data for model training. * Optimize and fine-tune models for performance, accuracy, and scalability. * Deploy machine learning models ...
Conduct data analysis and preprocessing to ensure high-quality data for model training. * Optimize and fine-tune models for performance, accuracy, and scalability. * Deploy machine learning models ...
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 ...
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 ...
This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work ...
This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work ...
Machine Learning Engineer
Arlington, VA · On-site
This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work ...
Machine Learning Engineer
Arlington, VA · On-site
This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work ...
This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work ...
This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
... training, creating reusable AI/ML artifacts, and working with team members to prototype new ... machine learning/statistical modeling data analysis tools and techniques - Current, active US ...
... training, creating reusable AI/ML artifacts, and working with team members to prototype new ... machine learning/statistical modeling data analysis tools and techniques - Current, active US ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
As a Machine Learning Engineer within the AWS Professional Services organization, you will be ... training, creating reusable AI/ML artifacts, and working with team members to prototype new ...
... training, creating reusable AI/ML artifacts, and working with team members to prototype new ... machine learning/statistical modeling data analysis tools and techniques - Current, active US ...
... training, creating reusable AI/ML artifacts, and working with team members to prototype new ... machine learning/statistical modeling data analysis tools and techniques - Current, active US ...
... training, creating reusable AI/ML artifacts, and working with team members to prototype new ... machine learning/statistical modeling data analysis tools and techniques - Current, active US ...
... training, creating reusable AI/ML artifacts, and working with team members to prototype new ... machine learning/statistical modeling data analysis tools and techniques - Current, active US ...
Machine Learning Engineer
Washington, DC · On-site +1
Build and maintain ML pipelines for training, evaluation, and inference * Integrate machine learning models into real-time and batch processing systems * Optimize model performance for accuracy ...
Machine Learning Engineer
Washington, DC · On-site +1
Build and maintain ML pipelines for training, evaluation, and inference * Integrate machine learning models into real-time and batch processing systems * Optimize model performance for accuracy ...
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 ...
Machine learning experience using visual data * Understanding of a variety of machine learning ... Experience curating quality, real-world datasets for training deep learning models * Proficiency in ...
Quick apply
Machine learning experience using visual data * Understanding of a variety of machine learning ... Experience curating quality, real-world datasets for training deep learning models * Proficiency in ...
Machine Learning Engineer
Arlington, VA · Hybrid
$110K - $160K/yr
Strong background in both classical and modern (deep learning) machine learning, including model selection, architecting, training, validation, testing, and deployment * Machine learning experience ...
Machine Learning Engineer
Arlington, VA · Hybrid
$110K - $160K/yr
Strong background in both classical and modern (deep learning) machine learning, including model selection, architecting, training, validation, testing, and deployment * Machine learning experience ...
Temporary Machine Learning Trainer information
What are the key skills and qualifications needed to thrive as a Temporary Machine Learning Trainer, and why are they important?
To thrive as a Temporary Machine Learning Trainer, you need a solid background in machine learning concepts, data analysis, and model evaluation, usually supported by a relevant degree or experience in computer science or a related field. Familiarity with programming languages like Python, machine learning libraries (such as TensorFlow or scikit-learn), and educational tools is typically required. Strong communication, adaptability, and instructional skills help trainers effectively convey complex topics and respond to diverse learner needs. These skills ensure trainees gain practical knowledge and confidence, contributing to successful training outcomes and organizational goals.
What are some common challenges faced by Temporary Machine Learning Trainers, and how can they be managed effectively?
Temporary Machine Learning Trainers often face the challenge of quickly adapting to new team environments and rapidly understanding existing workflows. Additionally, they may need to balance delivering training sessions with handling updates to curriculum or technology. Effective communication with permanent staff and staying up-to-date with the latest machine learning tools can help manage these challenges. Being proactive in seeking feedback and clarifying expectations early on can also contribute to a smoother transition and more impactful training sessions.
What are Temporary Machine Learning Trainers?
Temporary Machine Learning Trainers are professionals hired on a short-term or contract basis to develop, implement, and refine machine learning models or to train teams in machine learning techniques. Their responsibilities often include preparing training data, selecting appropriate algorithms, and ensuring models are accurate and efficient. They may also provide guidance to organizations on best practices and help upskill employees in machine learning concepts. These roles are typically project-based and may last from a few weeks to several months, depending on organizational needs.
What is the difference between Temporary Machine Learning Trainer vs Data Scientist?
| Aspect | Temporary Machine Learning Trainer | Data Scientist |
|---|---|---|
| Credentials | Relevant certifications (e.g., AWS, Google Cloud), technical training | Advanced degrees (Master's or PhD) in data science, statistics, or related fields |
| Work Environment | Training sessions, workshops, corporate training settings | Data analysis, modeling, research environments, often in offices or labs |
| Employer & Industry Usage | Tech companies, educational institutions, consulting firms | Tech, finance, healthcare, research organizations |
While both roles involve working with data and machine learning, a Temporary Machine Learning Trainer primarily focuses on educating and training teams or clients on machine learning tools and concepts. In contrast, a Data Scientist develops models, analyzes data, and derives insights for decision-making. The roles differ mainly in their focus—training versus data analysis—though they share foundational technical skills.
What are popular job titles related to Temporary Machine Learning Trainer jobs in Washington? For Temporary Machine Learning Trainer jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Temporary Machine Learning Trainer jobs in Washington look for? The top searched job categories for Temporary Machine Learning Trainer jobs in Washington are:
What cities in Washington are hiring for Temporary Machine Learning Trainer jobs? Cities in Washington with the most Temporary Machine Learning Trainer job openings:
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:
Qualifications:
Preferred Skills:
Position Clearance Requirement:
TS/SCI with Full-Scope Polygraph
This position is located in Chantilly/Herndon, VA.
We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.
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.
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.
We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.
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