Job Information
Shuvel Digital Sr. Machine Learning Engineer in Remote, Virginia
Sr. Machine Learning Engineer
Clearance Level: Top Secret (TS/SCI Eligible)
US Citizenship: Required
Job Classification: Full Time
Location: Remote
Years of Experience: 7 - 10 years
Education Level: Bachelor's Degree or equivalent experience
What Makes this a Great Opportunity:
An exciting remote telework opportunity, to work as a Sr. Machine Learning (ML) Engineer who will be responsible for research, development, implementing, and deploying machine learning models and algorithms to solve a wide range of cyber analytical challenges. You will collaborate with cross-functional teams to identify opportunities for applying machine learning techniques, collect and preprocess data, design, and train models, and deploy solutions into production environments. This role offers an exciting opportunity to work with state-of-the-art technologies and make a significant impact in the field of machine learning.
Position Description:
We are looking for a Machine Learning (ML) Engineer with documented expertise to be responsible for researching, developing, architecting, and integrating ML models, algorithms, tools, and techniques into existing or new environments. A candidate who will architect and implement machine learning and extract-transform-load (ETL) algorithms and conduct data integrity and validation actions. A candidate who will work with our data scientists to design, develop, and integrate ML models and algorithms to address specific problems, (e.g., classification, regression, clustering, recommendation systems, etc.), and introduce ML and pattern recognition to discover hidden insights. Successful candidates for this role must have critical thinking skills, be creative, curious, resourceful, and have a passion for conveying a wide range of information through research leading to deeper insights. The candidate may work independently but participate in project-wide reviews of requirements, system architecture, and detailed design documents. Our ML Engineer must collaborate well with a strong lean-forward attitude to shift knowledge left, deliver well, and produce quality results.
Ability/experience to research and develop algorithms to analyze structured cyber-security data, including supervised machine learning, entity resolution, classification, and the implementation of analytical algorithms on a distributed cloud-based infrastructure.
Assist and introduce ML and pattern recognition to discover hidden insights; architect and implement data processing, cleansing, and conducting data integrity and validation actions.
Exercise creativity in applying non-traditional approaches to the analysis of unstructured data in support of high-value use cases using multi-dimensional visualization.
Implement processing on high-volume, high-velocity data streams.
Requires strong technical and computational skills – engineering, physics, and mathematics, coupled with the ability to code design, develop, and deploy sophisticated applications using advanced structured data analysis techniques and utilizing high-performance computing environments.
Can utilize advanced tools and computational skills to interpret, connect, predict, and make discoveries in complex cyber-security data and deliver recommendations for business and analytic decisions.
Recommend and implement interactive reports, visual analytics, and dashboards focused primarily on understanding and using deep packet inspection of structured and unstructured collected digital data.
Work closely with data scientists, software developers, and project managers to understand requirements and identify opportunities for applying data analysis techniques.
Collect, preprocess, and analyze large datasets to extract meaningful insights and features for model training.
Collaborate with software developers to integrate data analytical solutions into production systems and applications.
Stay updated on the latest advancements in large data analytics and machine learning research and technologies and identify opportunities for innovation and improvement.
Demonstrate ability to research and apply new tools, techniques, and solution approaches. Continually learn and improve your skills through sharing with others and taking advantage of available training sources.
Required Skills:
Experience working with machine learning, data science, or related fields.
Experience working with cyber-security data.
Experience in statistical analysis and visualization of complex data.
An understanding and ability to implement data hygiene methods via ETL.
Ability to build upon previous analytics capabilities to enable more complex analysis of large datasets, including graphs to generate actionable intelligence.
Solid understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, reinforcement learning, etc.
Hands-on experience with popular machine learning libraries and frameworks, (e.g., TensorFlow, PyTorch, Scikit-learn, Keras, SciPy, etc.).
Experience with data processing and analysis tools, (e.g., Python, Sci-Kit, NumPy, SQL, Spark, etc.).
Excellent problem-solving and analytical skills, with a keen attention to detail.
Strong communication and collaboration skills, with the ability to independently or to work effectively in a team environment; the ability to quickly adapt to changing priorities/requirements.
Experience with network traffic inspection tools (e.g., Suricata, Arkime, Zeek, etc.).
Desired Skills:
Experience with Apache Spark, Apache Streaming, and Jupyter Hub+ Event Reporter to introduce ML and pattern recognition to discover hidden insights.
Working knowledge of networks, network traffic data, and virtual environments.
Experience with containerization (e.g., Docker, Kubernetes, Rancher, etc.).
Experience with ML methods (e.g., decision trees, neural networks, reinforcement learning, etc.).
An understanding and working knowledge to analyze and gain visibility to network metadata, and content, identify malicious code, anomaly detection, and potentially predictive analysis.
Working knowledge in programming languages, (e.g., Python, Rust, Go, Java, etc.).
Working knowledge with cloud computing platforms, (e.g., AWS, Azure, or Google Cloud, etc.).
Familiarity with big data technologies, (e.g., Elastic Search, Apache Hadoop, Spark, Kafka, etc.).
Experience deploying ML models in production environments using containerization technologies, (e.g., Docker, Kubernetes).
Publications or contributions to ML-related research projects or open-source initiatives.
