Machine Learning Engineer

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. 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.

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