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 ...
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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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Mentor junior engineers and guide best practices in ML development Stay current with emerging ML ... Strong understanding of machine learning theory and fundamentals * Model selection and evaluation
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Junior Machine Learning Compiler Engineer information
What are typical projects and responsibilities for a Junior Machine Learning Compiler Engineer in a collaborative team setting?
What does a Junior Machine Learning Compiler Engineer do?
What are the key skills and qualifications needed to thrive as a Junior Machine Learning Compiler Engineer, and why are they important?
What is the difference between Junior Machine Learning Compiler Engineer vs Data Scientist?
| Aspect | Junior Machine Learning Compiler Engineer | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's in Computer Science, Software Engineering, or related field; knowledge of compiler design and ML frameworks | Bachelor's or higher in Data Science, Statistics, Computer Science, or related field; strong analytical skills |
| Work Environment | Software development teams, focusing on compiler optimization for ML models | Data analysis teams, focusing on data interpretation and model development |
| Employer & Industry Usage | Tech companies, AI startups, hardware firms | Tech firms, finance, healthcare, research institutions |
The Junior Machine Learning Compiler Engineer primarily focuses on developing and optimizing compilers for machine learning models, requiring programming and compiler knowledge. In contrast, a Data Scientist analyzes data, builds models, and provides insights. Both roles are essential in AI and tech industries but differ in technical focus and daily tasks.
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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.Â
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.Â
Position Clearance Requirement:
TS/SCI with Full-Scope Polygraph
This position is located in Chantilly/Herndon, VA.Â
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