VP of R&D

Our technology brings together large volumes of fragmented data across different intelligence domains, helping investigators and analysts uncover connections, accelerate investigations and make better-informed decisions.

We operate with the ambition of a technology company that wants to lead the market, not simply follow it. Speed, experimentation, technical creativity and the ability to turn difficult ideas into real capabilities are fundamental to how we work.

The Role

We are looking for an experienced VP of R&D to lead and scale TRG’s engineering organization through its next stage of growth.

This is not a role focused only on managing delivery, processes or people. We are looking for a strong engineering leader who combines technical depth, product thinking, execution, innovation and people leadership.

You will lead our engineering organization across several technology domains, including Big Data and Data Fusion, AI, Geospatial Intelligence and OSINT. You will be directly involved in our Big Data, Data Fusion and AI engineering area while managing and developing the engineering leaders responsible for the other pillars.

A key part of the role is helping us scale without losing our startup mindset. We want stronger engineering foundations, leadership and accountability, without introducing unnecessary layers, processes or structures that slow down experimentation and delivery.

You will work closely with the CPO, CTO, Product, Engineering Leaders and Applied Research  to turn ambitious ideas, research and prototypes into scalable products and continuously strengthen TRG’s technical and market advantage.

The role reports directly to the Chief Product Officer and leads TRG’s engineering managers and technical leadership team.

What You’ll Do

Lead and scale the R&D organization

• Lead the engineering organization across Big Data & Data Fusion, AI, Geospatial Intelligence, OSINT and other emerging technology areas.
• Be directly involved in the leadership and technical direction of our Big Data, Data Fusion and AI engineering area.
• Manage and develop the engineering leaders responsible for the other product and technology pillars.
• Build a strong engineering leadership layer that can operate with increasing independence and ownership.
• Coach engineering managers and technical leaders, helping them strengthen their technical leadership, product thinking, decision-making and ability to build high-performing teams.
• Create clear ownership, accountability and decision-making without unnecessary management layers.
• Scale the organization while protecting speed, autonomy and the ability to experiment.
• Build an engineering culture based on ownership, high standards, collaboration and continuous delivery of value.
• Identify capability gaps early and build the teams, leadership and technical skills needed for the company’s next stage of growth.

Define and challenge the technical direction

• Own the engineering and technology direction in close partnership with Product and technical leadership.
• Stay close to architecture and important technical decisions rather than operating only through management layers.
• Challenge unnecessary complexity, repeated refactoring, weak architectural decisions and technical choices that create long-term delays.
• Provide strong technical direction across complex systems involving large-scale data ingestion, data fusion, distributed processing, analytics, AI and geospatial intelligence.
• Ensure our architecture supports increasing data volumes, new intelligence sources, new products and the rapid introduction of new capabilities.
• Guide architectural decisions across backend, data, AI, infrastructure and platform engineering.
• Make pragmatic trade-offs between scalability, reliability, security, maintainability and speed.
• Know when production-grade engineering is required and when an MVP or experiment is the right approach to validate an idea.
• Identify technical risks early and ensure teams take ownership of solving them.

Strengthen execution

• Translate company and product strategy into clear engineering priorities, plans and outcomes.
• Build an engineering organization that continuously delivers meaningful product value rather than optimizing only for output or delivery metrics.
• Improve execution, quality and predictability where needed without turning engineering into a process-heavy organization.
• Remove unnecessary dependencies, processes and organizational barriers that slow teams down.
• Establish appropriate standards for reliability, observability, testing, release management, security and operational readiness.
• Help teams balance new capabilities with scalability, stability and technical-debt reduction.
• Create transparency around progress, risks, dependencies and engineering capacity while maintaining strong team ownership.
• Use AI and modern engineering tools aggressively to increase engineering productivity and shorten the path from idea to production.

Connect Product and Engineering

• Build a strong connection between engineering decisions, product strategy and real customer problems.
• Ensure engineers understand why we are building something, not only what they have been asked to implement.
• Work closely with Product to find the best solution rather than treating requirements as fixed instructions.
• Challenge Product when technology can unlock a better solution, and challenge Engineering when technical complexity is preventing product progress.
• Encourage engineering leaders and teams to think about customer value, competitive advantage and product outcomes.
• Help turn prototypes, research and experimental capabilities into scalable products.

Drive innovation and technical advantage

• Make innovation a core responsibility of the R&D organization, not a side activity.
• Push the organization beyond implementing capabilities that already exist in the market.
• Work closely with Product, Applied Research and engineering teams to identify opportunities where TRG can be first to market.
• Encourage exploration across areas such as AI, Big Data, Geospatial Intelligence, OSINT and emerging technologies relevant to our products.
• Create an environment where teams can quickly prototype, test and learn from ambitious ideas.