TRG Research and Development
51 - 200 employees
💼 Consulting
🔒 Cybersecurity
📦 Logistics
Consulting • Cybersecurity • Logistics
TRG Research and Development is a company specializing in data fusion technology, providing an all-in-one platform called Intellectus. This platform offers real-time intelligence and insights for government, law enforcement agencies, public safety organizations, banks, and financial institutions. With capabilities like lawful interception, financial investigations, forensic data integration, and tactical systems integration, TRG's solutions are designed to enhance investigations, fraud detection, and decision-making processes. Their products are trusted by numerous analysts worldwide to safeguard organizations and improve operational efficiency.
VP of R&D
🔥 3 minutes ago
Where this job comes from
Posted on TRG Research and Development's careers page, not through a job board
First seen by Remote Rocketship 3 minutes ago
TRG Research and Development
51 - 200 employees
💼 Consulting
🔒 Cybersecurity
📦 Logistics
Consulting • Cybersecurity • Logistics
TRG Research and Development is a company specializing in data fusion technology, providing an all-in-one platform called Intellectus. This platform offers real-time intelligence and insights for government, law enforcement agencies, public safety organizations, banks, and financial institutions. With capabilities like lawful interception, financial investigations, forensic data integration, and tactical systems integration, TRG's solutions are designed to enhance investigations, fraud detection, and decision-making processes. Their products are trusted by numerous analysts worldwide to safeguard organizations and improve operational efficiency.
📋 Description
• Lead and scale TRG’s R&D organization across Big Data and Data Fusion, AI, Geospatial Intelligence, OSINT and emerging technology areas • Provide direct leadership and technical direction for the Big Data, Data Fusion and AI engineering area • Manage and develop engineering leaders responsible for other product and technology pillars • Build a strong engineering leadership layer with increasing independence and ownership • Coach engineering managers and technical leaders on technical leadership, product thinking, decision-making and team building • Establish ownership, accountability and decision-making without unnecessary management layers • Scale the organization while protecting speed, autonomy and experimentation • Build an engineering culture based on ownership, high standards, collaboration and continuous value delivery • Own and challenge engineering and technology direction with Product and technical leadership • Guide architecture across backend, data, AI, infrastructure and platform engineering • Ensure architecture supports increasing data volumes, intelligence sources, products and new capabilities • Make pragmatic trade-offs among scalability, reliability, security, maintainability and speed • Identify technical risks early and ensure teams address them • Translate company and product strategy into engineering priorities, plans and outcomes • Improve execution, quality and predictability without creating a process-heavy organization • Establish standards for reliability, observability, testing, release management, security and operational readiness • Balance new capabilities with scalability, stability and technical-debt reduction • Use AI and modern engineering tools to increase productivity and shorten the path from idea to production • Connect engineering decisions with product strategy and real customer problems • Turn prototypes, research and experimental capabilities into scalable products • Drive innovation across AI, Big Data, Geospatial Intelligence, OSINT and emerging technologies • Partner with Product, Operations, Customer Delivery, Intelligence Solutions and commercial teams • Communicate technical direction, priorities, risks and trade-offs to executive and non-technical stakeholders • Contribute to company-level strategy and planning as a senior leadership team member • Report directly to the Chief Product Officer and lead engineering managers and the technical leadership team
🎯 Requirements
• Significant experience leading and scaling a complex engineering organization of approximately 80–150+ engineers, including engineering managers and senior technical leaders • Previous experience as a Head of R&D/Engineering, VP Engineering, Engineering Director, Group Engineering Manager, or comparable role • Demonstrated ability to scale an engineering organization without losing speed, ownership, technical depth and a startup mindset • Strong track record of developing engineering managers and technical leaders into strong, independent leaders • Strong software engineering and architecture background • Strong understanding of complex, data-intensive and distributed systems • Experience with Big Data, data fusion, data modelling, distributed systems, data pipelines, analytics, AI systems and large-scale data processing • Experience owning technical strategy and architecture decisions across multiple teams or product areas • Strong product mindset and ability to connect technical decisions with customer, product and business outcomes • Strong track record of improving engineering execution, quality, accountability and delivery • Pragmatic decision-maker able to balance building perfectly with getting valuable products to users quickly • Strong bias toward action, experimentation and problem-solving • Understanding of how AI is changing software engineering and how engineering organizations can use it to increase capabilities and speed • Clear and direct communication skills for aligning engineers, product leaders and executives • Comfort operating in a fast-moving, international environment • Strong advantages: experience with investigation, intelligence, analytics, cybersecurity, fraud-detection or decision-support platforms • Strong advantages: geospatial intelligence, location intelligence, mapping, movement analysis or geospatial data systems • Strong advantages: data-fusion products combining structured and unstructured information • Strong advantages: AI/ML systems, LLM-based capabilities or AI-native product development • Strong advantages: moving prototypes or research projects into scalable production products • Strong advantages: distributed engineering teams across multiple countries and time zones
🏖️ Benefits
• Startup environment with speed, experimentation, technical creativity and autonomy • Direct influence on company strategy, product direction and technical foundations • Freedom to challenge existing approaches and introduce better ways of building products and technology • Opportunity to work on difficult technical problems involving Big Data, AI, Data Fusion, Geospatial Intelligence and OSINT • Close collaboration with Product and Applied Research • Opportunity to help build capabilities that can define new markets
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