Senior Applied AI Researcher
Posted 17 days ago · 0 applicants
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The role in plain words
This role focuses on designing, evaluating, and deploying AI systems that analyze complex security data and automate workflows. You will build reliable agent-based systems, develop semantic models, and work on problems like relational discovery and graph-based retrieval. Day-to-day tasks include running benchmarking frameworks, experimenting with agent architectures, and collaborating with engineering to move research prototypes into production.
- Strong background in applied AI, machine learning, or AI systems
- MSc or PhD in Computer Science, Machine Learning, AI, or a related field, or equivalent practical experience
- Hands-on experience with LLMs, AI agents, or complex AI systems
- Experience designing evaluation and benchmarking methodologies for AI systems
- Experience working with structured and semi-structured data systems
- Familiarity with knowledge graphs, graph databases, or graph-based reasoning
- Experience evaluating system-level behavior of AI systems in production
- Publications, open-source contributions, or prior research in AI systems, agents, or data reasoning systems
Extracted from the job description · kept up to date automatically
Who this suits
This role suits individuals with a strong background in applied AI or machine learning, hands-on experience with LLMs or AI agents, and proficiency in Python. It is ideal for those holding an advanced degree or equivalent practical experience who can work independently on ambiguous, real-world problems.
Full job description
Original listing · kept for referenceWhy Sola?
Because we believe AI should empower, not complicate. At Sola, you’ll work on a platform that simplifies cybersecurity for practitioners everywhere, combining cutting-edge technology with user-first design. Learn more about it here.
We are looking for a Senior Applied AI Researcher to design, evaluate, and deploy AI systems that operate over complex security data and workflows.
This role focuses on building reliable agent-based systems, developing semantic representations of security data, and enabling AI systems to reason across structured and unstructured sources. The work spans research, experimentation, and productionization.
You will work on problems such as relational discovery, schema alignment, semantic modeling, and graph-based retrieval, and turn promising approaches into real systems used by security teams.
What You’ll Do
• Design and build AI systems for security analysis and automation.
• Develop methods for relational discovery, schema matching, and semantic modeling across heterogeneous security data.
• Design and run evaluation and benchmarking frameworks for models, agents, and end-to-end systems.
• Experiment with agent architectures, tools, and orchestration strategies.
• Investigate system behavior, analyze failure modes, and improve system reliability.
• Collaborate with engineering teams to bring research prototypes into production systems.
Requirements:
• Strong background in applied AI, machine learning, or AI systems.
• MSc or PhD in Computer Science, Machine Learning, AI, or a related field, or equivalent practical experience.
• Hands-on experience with LLMs, AI agents, or complex AI systems.
• Experience designing evaluation and benchmarking methodologies for AI systems.
• Experience working with structured and semi-structured data systems.
• Proficiency in Python and modern AI frameworks.
• Ability to work independently on ambiguous, real-world problems.
Preferred
• Familiarity with knowledge graphs, graph databases, or graph-based reasoning.
• Experience applying AI in security or adversarial environments.
• Experience evaluating system-level behavior of AI systems in production.
• Publications, open-source contributions, or prior research in AI systems, agents, or data reasoning systems.
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- Strong background in applied AI, machine learning, or AI systems, MSc or PhD in Computer Science, Machine Learning, AI, or a related field, or equivalent practical experience, Hands-on experience with LLMs, AI agents, or complex AI systems, Experience designing evaluation and benchmarking methodologies for AI systems, Experience working with structured and semi-structured data systems