As a Senior Security Researcher, you will be at the forefront of cyber defense, focusing on vulnerability research, reverse engineering, and exploit development.
Compensation
$139,600.00 - $225,775.00/yr
Responsibilities
Conduct advanced vulnerability research, reverse engineering, fuzzing, root-cause analysis, and exploit development across various platforms.
Leverage Large Language Models (LLMs) and advanced ML techniques to automate and scale bug hunting across diverse domains, including Web, OS & Kernel, Software Supply Chain, OT/IoT, and Browser Security.
Use AI agents to accelerate PoC generation, exploitability analysis, and finding validation, with an emphasis on reducing false positives.
Design and deploy AI-guided fuzzers, smart static/dynamic analysis tools, and autonomous agentic workflows specifically built for vulnerability research.
Develop and utilize agent harnesses for vulnerability discovery, PoC validation, patch validation, and defensive security research workflows.
Requirements
Bachelor's degree in Computer Science, Cybersecurity, or a related technical field
Data Scientist supporting a government contract in Washington, DC, building dashboards, predictive models, and AI/ML solutions.
Compensation
$60,000—$180,000 USD
Responsibilities
Builds, maintains, and improves dashboards, applications, and automations within the Power Platform (PowerBI, Power Automate, and PowerApps), SharePoint, and InfoPath environments to reduce or eliminate manual effort in data processing by automating common processes.
Connects to and leverages application programming interfaces (APIs), system-to-system connections, and manual transfer of data as part of business intelligence extract, transform, and load (ETL).
Builds predictive risk models backed by statistical analysis and supports data-driven solutions.
Identifies and applies usages of AI and ML to extract key insights from unstructured system documentation for analysis, to automate processes, and to integrate with other applications.
Develops and carries out full-service data calls, including creation of collection applications and forms, data normalization, data cleansing, and visualization.
A key technical leadership role in advancing data-driven engineering capabilities for HBM products using advanced data analytics, machine learning, and AI to improve product yield, quality, reliability, and engineering efficiency.
Responsibilities
Apply statistical analysis, machine learning, AI, and engineering domain knowledge to analyze complex engineering data to identify yield and reliability signatures.
Lead and contribute to cross-functional initiatives to identify yield opportunities and drive sustainable improvements in overall yield, quality, and product performance.
Develop, validate, and deploy advanced algorithms, including machine learning and deep learning models, for data mining, pattern recognition, and anomaly detection.
Develop scalable analytics and early-warning solutions to proactively identify yield, quality, product, and process deviations.
Drive improvements in data infrastructure, automation, visualization, and analytics workflows.
Requirements
Bachelor's or Master's degree in Electrical/Electronic Engineering
Lead technical engineer responsible for researching, developing, evaluating, and integrating generative AI and agentic AI capabilities for U.S. government agencies.
Compensation
$174,000 - $217,500 - $261,000 Annual
Responsibilities
Research, develop, evaluate, and integrate generative AI and agentic AI capabilities
Provide technical leadership to cross‑functional teams
Analyze data and language processing needs and recommend innovative solutions
Prototype AI/GenAI systems or components
Evaluate technology solutions and provide actionable recommendations
Requirements
Bachelor’s degree in computer science, engineering, computational linguistics, or related disciplines
Master’s degree in computer science, engineering, computational linguistics, or related disciplines
PhD in computer science, engineering, computational linguistics, or related disciplines
Nice to have: advanced degree in computer science, engineering, computational linguistics, or related disciplines
Nice to have: track‑record of successful deployment of agentic, generative, and HLT systems
The Senior Data Analytical Specialist/Scientist will develop advanced analytics, predictive models, and AI-driven insights to address priority business opportunities, applying statistical, machine learning, and analytical techniques to enterprise data.
Responsibilities
Design and develop advanced analytics, predictive models, and machine learning solutions for priority business use cases.
Conduct exploratory data analysis to identify patterns, trends, relationships, and opportunities for improved decision-making.
Translate business problems into appropriate analytical and modelling approaches, defining hypotheses, methodologies, and success measures.
Assess the quality, availability, and readiness of data required for AI and advanced analytics use cases.
Support enterprise data maturity and analytics capability assessments and identify opportunities for improvement.
Requirements
Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent experience.
Senior Quantitative Analyst – New Product Development – Morningstar Indexes
Responsibilities
Build innovative index solutions through rigorous investment research, and own it through full product development lifecycle from ideation, design, development, and validation, leading up to launch
Collaborate effectively with the broader Morningstar research organization including equity, quantitative, manager or sustainability research teams, and product management to develop novel thematic and smart beta indexes that leverage Morningstar IP
Promote integration of team throughput and operational processes with other index teams to facilitate growth of our product and methodology suite
Help develop new tools and capabilities to perform portfolio construction or analytics independently, including the use of statistical and machine learning techniques
Publish white papers, factsheets, client presentations, and other collateral to support go-to-market plans
Requirements
Bachelor’s degree in quantitative, financial discipline, or engineering
MBA/Masters degree
Nice to have: CFA charter or candidature (at least passed Level II)
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