Careers at SPARC
Be on the forefront of breakthrough research in AI
Why work at SPARC?
At SPARC, we solve problems of national and strategic importance – building AI infrastructure that must be secure and efficient by design, verifiable at scale, and resilient to advanced threats. This is a place for those who want to move beyond incremental work and contribute to systems that matter.
Be on the cutting edge of AI innovation
Best in class compensation with equity options
Life at SPARC
Open positions
We are continuously looking for researchers, engineers, and technologists who want to work on foundational challenges in secure and sovereign AI.
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Principal Researcher – AI
Date posted : Mar 09, 2026
Job description:
Role overview
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Key responsibilities
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Qualifications
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About SPARC
SPARC is an AI research and engineering lab focused on building secure, sovereign, and continuously improving AI systems.
We operate at the frontier of AI and security, driven by a two-fold mission:
- Self-improving AI: We advance AI systems that improve through experience—learning from interaction, refining their reasoning, and growing more capable over time.
- Secure-by-Design Infrastructure: We develop the foundations for AI infrastructure that is secure by design, verifiable at scale, and resilient enough for deployment in critical sectors such as defence, healthcare, finance, and national infrastructure.
Cybersecurity serves as our proving ground. It provides objective feedback, rapidly evolving environments, and adaptive adversaries, making it one of the most demanding domains for developing autonomous AI systems. Success in this environment pushes the frontier of AI itself, advancing systems that can reason, adapt, and operate reliably in complex real-world settings.
At SPARC, you'll work on fundamental research and engineering challenges while building AI systems designed to solve problems of national and strategic importance. This is a place for those who want to move beyond incremental work and contribute to systems that matter.
Role Overview
As a Research Scientist, you will develop novel algorithms and systems for autonomous AI agents in cybersecurity. The role focuses on advancing foundation models, autonomous AI agents, reasoning systems, reinforcement learning, and continual adaptation for complex real-world environments.
You will collaborate closely with other AI researchers, engineers, and cybersecurity experts to transform cutting-edge research into practical, high-impact systems. This is a research-focused role with significant opportunities to influence the future of AI agents, long-horizon reasoning and their applications in cybersecurity.
What You'll Work On
Post-Training and Reasoning
- Develop novel post-training techniques for reasoning models, including reinforcement learning and synthetic data generation.
- Improve model capabilities for planning, tool use, long-horizon reasoning, and decision-making.
- Research continual and experiential learning methods for self-evolving AI systems.
Agent Systems and Harness Optimization
- Design and build autonomous AI agents capable of operating in complex environments.
- Develop algorithms for agent optimization, including memory systems, verifiers, self-reflection, planning, and tool orchestration.
- Research methods for improving reliability, robustness, and generalization of AI agents.
Evaluation and Benchmarking
- Design rigorous evaluation methodologies and benchmarks for reasoning models and AI agents.
- Build scalable infrastructure for measuring agent performance, reliability, and safety.
- Develop datasets and environments that enable reproducible research on autonomous systems.
Research and Impact
- Build research artifacts and contribute to the broader AI community.
- Translate research breakthroughs into deployable AI systems used in real-world cybersecurity settings.
- Develop datasets and environments that enable reproducible research on autonomous systems.
Qualifications
We are looking for researchers with deep expertise in one or more of the following areas:
- Large Language Models and Post-Training
- Reinforcement Learning and RL for Reasoning
- Agentic AI (tool use, memory, planning, verification)
- AI Evaluation and Benchmarking
- Machine Learning and Foundation Models
- Probabilistic Reasoning and Decision Making
- Distributed AI Systems and Inference Infrastructure
Candidates should hold a PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or possess equivalent research experience.
A strong publication record in leading conferences such as NeurIPS, ICML and ICLR is highly desirable.
Why Join SPARC?
- Work on some of the hardest open problems in AI agents and reasoning.
- Build AI systems that operate in real-world, high-stakes environments.
- Collaborate with researchers and engineers across AI and cybersecurity.
- Help define the future of autonomous cybersecurity systems through an open research environment and collaborations.
Ideal candidates are excited by questions such as:
- How do we train reasoning models that can solve long-horizon tasks?
- How do we build self-improving agents with memory and verification?
- How do we rigorously evaluate autonomous AI systems?
- How do we make AI agents reliable enough for real-world cybersecurity?
About SPARC
SPARC is an AI research and engineering lab focused on building secure, sovereign, and continuously improving AI systems.
We operate at the frontier of AI and security, driven by a two-fold mission:
- Self-improving AI: We advance AI systems that improve through experience—learning from interaction, refining their reasoning, and growing more capable over time.
- Secure-by-Design Infrastructure: We develop the foundations for AI infrastructure that is secure by design, verifiable at scale, and resilient enough for deployment in critical sectors such as defence, healthcare, finance, and national infrastructure.
Cybersecurity serves as our proving ground. It provides objective feedback, rapidly evolving environments, and adaptive adversaries, making it one of the most demanding domains for developing autonomous AI systems. Success in this environment pushes the frontier of AI itself, advancing systems that can reason, adapt, and operate reliably in complex real-world settings.
At SPARC, you'll work on fundamental research and engineering challenges while building AI systems designed to solve problems of national and strategic importance. This is a place for those who want to move beyond incremental work and contribute to systems that matter.
Role Overview
As a Principal Research Scientist, you will define the long-term scientific direction of SPARC's AI research program. You will lead ambitious research initiatives at the frontier of reasoning models, autonomous AI agents, reinforcement learning, and continual adaptation, with a focus on advancing AI capabilities for cybersecurity and other high-impact domains.
You will identify the most important open research problems, develop novel algorithms and systems to address them, and guide multidisciplinary teams from fundamental research through real-world deployment. Working closely with engineers and cybersecurity experts, you will help shape both the scientific vision and technical strategy of the organization.
This role is intended for established researchers who aspire to build a world-class AI research lab while contributing original research that advances the state of the art.
What You'll Work On
Research Vision
- Define the long-term research agenda across reasoning models, autonomous agents, reinforcement learning, and AI systems.
- Identify high-impact research directions and drive breakthrough advances in frontier AI.
- Shape the scientific strategy of the organization and ensure research efforts align with long-term objectives.
Research Leadership
- Lead multidisciplinary research programs spanning foundation models, post-training, agent architectures, evaluation, and continual learning.
- Mentor research scientists and foster a culture of scientific rigor, curiosity, and technical excellence.
- Establish research methodologies, evaluation standards, and best practices across projects.
Scientific Impact
- Publish influential research in leading AI conferences and journals.
- Build collaborations with academia, industry, and government research organizations.
- Represent SPARC through invited talks, technical leadership, open-source contributions, and engagement with the broader AI research community.
Translating Research into Impact
- Work closely with engineering teams to transform research advances into scalable, deployable AI systems.
- Guide the transition from research prototypes to production capabilities while maintaining scientific integrity.
- Help build AI systems that solve challenging real-world cybersecurity problems.
Qualifications
Candidates should hold a PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field, with an outstanding record of research contributions.
We are particularly interested in researchers with expertise in several of the following areas:
- Large Language Models and Post-Training
- Reinforcement Learning and Reasoning
- Autonomous AI Agents
- AI Systems and Agent Architectures
- Continual Learning and Self-Improving Systems
- AI Evaluation and Benchmarking
- Probabilistic Reasoning and Decision Making
A demonstrated track record of scientific leadership—including leading research programs, mentoring researchers, publishing influential work, and shaping research strategy—is essential.
Why Join SPARC?
- Work on some of the hardest open problems in AI agents and reasoning.
- Build AI systems that operate in real-world, high-stakes environments.
- Collaborate with researchers and engineers across AI and cybersecurity.
- Help define the future of autonomous cybersecurity systems through an open research environment and collaborations.
Ideal candidates are excited by questions such as:
- How do we train reasoning models that can solve long-horizon tasks?
- How do we build self-improving agents with memory and verification?
- How do we rigorously evaluate autonomous AI systems?
- How do we make AI agents reliable enough for real-world cybersecurity?
About SPARC
SPARC is an AI research and engineering lab focused on building secure, sovereign, and continuously improving AI systems.
We operate at the frontier of AI and security, driven by a two-fold mission:
- Self-improving AI: We advance AI systems that improve through experience—learning from interaction, refining their reasoning, and growing more capable over time.
- Secure-by-Design Infrastructure: We develop the foundations for AI infrastructure that is secure by design, verifiable at scale, and resilient enough for deployment in critical sectors such as defence, healthcare, finance, and national infrastructure.
Cybersecurity serves as our proving ground. It provides objective feedback, rapidly evolving environments, and adaptive adversaries, making it one of the most demanding domains for developing autonomous AI systems. Success in this environment pushes the frontier of AI itself, advancing systems that can reason, adapt, and operate reliably in complex real-world settings.
At SPARC, you'll work on fundamental research and engineering challenges while building AI systems designed to solve problems of national and strategic importance. This is a place for those who want to move beyond incremental work and contribute to systems that matter.
Role Overview
As an AI Engineer, you will build the software systems that power next-generation AI for cybersecurity. You will develop the infrastructure that enables rapidly training, evaluating, and deploying large language models and autonomous AI agents at scale.
This role spans AI infrastructure, distributed systems, model serving, evaluation platforms, and agent runtimes. You will work closely with research scientists to transform cutting-edge algorithms into reliable, production-quality systems.
What You'll Work On
AI Infrastructure
- Build scalable infrastructure for training, evaluating, and deploying foundation models and autonomous AI agents.
- Develop high-performance inference and model serving systems for large language models.
- Design distributed compute, storage, and orchestration systems for AI workloads.
Agent Platforms
- Build agent runtimes supporting planning, tool use, memory, verification, and long-horizon execution.
- Develop infrastructure for agent orchestration, task scheduling, and execution across diverse environments.
- Create reusable frameworks that enable rapid experimentation with new agent capabilities.
Evaluation and Experimentation
- Build scalable evaluation pipelines and benchmark infrastructure for AI models and autonomous agents.
- Develop tooling for experiment management, data collection, telemetry, and performance analysis.
- Improve reproducibility, observability, and reliability of AI experimentation.
Production Systems
- Productionize research prototypes into robust, maintainable software systems.
- Build APIs, developer tools, and internal platforms that accelerate AI research and deployment.
- Optimize systems for performance, scalability, and reliability across large-scale deployments.
Qualifications
We are looking for engineers with strong software engineering fundamentals and expertise in one or more of the following areas:
- AI/ML Systems
- Large Language Model Inference and Serving
- Distributed Systems
- Agent Frameworks and AI Infrastructure
- Cloud Infrastructure and Container Orchestration
- Data Processing Pipelines
- Python, C++, Go, or Rust
Experience with modern AI infrastructure such as distributed training, model serving, inference optimization, evaluation frameworks, or large-scale production AI systems is highly desirable.
Quote (for website and social media): This is not a typical ML engineering role. You will build the infrastructure behind frontier AI research—agent runtimes, evaluation platforms, model serving systems, and distributed software that enables researchers to iterate rapidly on the next generation of reasoning models and autonomous AI agents.
Why Join SPARC?
- Work on some of the hardest open problems in AI agents and reasoning.
- Build AI systems that operate in real-world, high-stakes environments.
- Collaborate with researchers and engineers across AI and cybersecurity.
- Help define the future of autonomous cybersecurity systems through an open research environment and collaborations.
Ideal candidates are excited by questions such as:
- How do we train reasoning models that can solve long-horizon tasks?
- How do we build self-improving agents with memory and verification?
- How do we rigorously evaluate autonomous AI systems?
- How do we make AI agents reliable enough for real-world cybersecurity?
About SPARC
SPARC is an AI research and engineering lab focused on building secure, sovereign, and continuously improving AI systems.
We operate at the frontier of AI and security, driven by a two-fold mission:
- Self-improving AI: We advance AI systems that improve through experience—learning from interaction, refining their reasoning, and growing more capable over time.
- Secure-by-Design Infrastructure: We develop the foundations for AI infrastructure that is secure by design, verifiable at scale, and resilient enough for deployment in critical sectors such as defence, healthcare, finance, and national infrastructure.
Cybersecurity serves as our proving ground. It provides objective feedback, rapidly evolving environments, and adaptive adversaries, making it one of the most demanding domains for developing autonomous AI systems. Success in this environment pushes the frontier of AI itself, advancing systems that can reason, adapt, and operate reliably in complex real-world settings.
At SPARC, you'll work on fundamental research and engineering challenges while building AI systems designed to solve problems of national and strategic importance. This is a place for those who want to move beyond incremental work and contribute to systems that matter.
Role Overview
As a Principal AI Engineer, you will define and lead the technical architecture for SPARC's AI platform. You will build the infrastructure that enables researchers to develop, evaluate, and deploy state-of-the-art reasoning models and autonomous AI agents, while ensuring these systems are scalable, reliable, and secure.
You will drive technical strategy across the AI stack—from distributed infrastructure and model serving to agent runtimes, evaluation platforms, and deployment systems. Working closely with research scientists, you will transform cutting-edge AI advances into production-grade capabilities that can operate in demanding real-world cybersecurity environments.
What You'll Work On
AI Platform Architecture
- Define the architecture for SPARC's AI platform, including training infrastructure, model serving, evaluation systems, and agent runtimes.
- Design scalable distributed systems capable of supporting frontier AI research and production deployments.
- Drive architectural decisions that balance research velocity with reliability, maintainability, and operational excellence.
AI Systems Engineering
- Lead the development of production infrastructure for large language models and autonomous AI agents.
- Build reusable platforms for experimentation, deployment, monitoring, and lifecycle management of AI systems.
- Ensure AI platforms are performant, observable, fault-tolerant, and secure.
Technical Leadership
- Establish engineering best practices for software quality, testing, scalability, security, and operational reliability.
- Mentor senior engineers and help grow a high-performing AI infrastructure team.
- Drive technical execution across multiple projects while maintaining a coherent long-term architecture.
Research Enablement
- Partner closely with AI researchers to rapidly translate new algorithms into production-ready systems.
- Build platforms that enable continuous improvement of reasoning models and autonomous agents.
Qualifications
We're looking for engineers who have built and led large-scale AI or distributed systems in research or production environments.
Strong expertise in several of the following areas is desirable:
- AI Infrastructure and ML Systems
- Large Language Model Serving and Inference
- Distributed Systems and High-Performance Computing
- Cloud Infrastructure and Container Orchestration
- Agent Platforms and Runtime Systems
- Data Infrastructure and Evaluation Platforms
- Software Architecture and Platform Engineering
Experience leading engineering teams, defining technical strategy, and delivering production AI platforms at scale is essential.
Quote (for website and social media): As a Principal AI Engineer, you'll shape the technical foundation of the company. The platforms you build will determine how quickly researchers can develop new reasoning models, how reliably autonomous agents operate, and how rapidly cutting-edge AI research becomes real-world cybersecurity capability.
Why Join SPARC?
- Work on some of the hardest open problems in AI agents and reasoning.
- Build AI systems that operate in real-world, high-stakes environments.
- Collaborate with researchers and engineers across AI and cybersecurity.
- Help define the future of autonomous cybersecurity systems through an open research environment and collaborations.
Ideal candidates are excited by questions such as:
- How do we train reasoning models that can solve long-horizon tasks?
- How do we build self-improving agents with memory and verification?
- How do we rigorously evaluate autonomous AI systems?
- How do we make AI agents reliable enough for real-world cybersecurity?