Senior Research Engineer | Cybersecurity AI AppSec
LocationParis Office
Work modehybrid
Typefull-time
SenioritySenior
Experience4+ yrs
DepartmentTech
EquityIncluded
Company size11–50 people
PostedSep 17, 2026 · 3w ago
Verified live12h ago
At a glanceSummarised by Seekless from the posting.
Must have7
4+ years of experience in backend engineering, security research, or applied R&D in a product environment
Strong software engineering fundamentals and ability to work across domains (systems, networking, web architectures)
Proficient in at least one backend language (Python, Go, or similar)
Experience designing and shipping production systems with measurable impact
Ability to translate complex ideas or research into reliable, scalable implementations
Strong analytical mindset, with a focus on pragmatism and impact
Comfortable collaborating across engineering and security to deliver end-to-end solutions
Nice to have3
Experience in application security (web security, pentesting, bug bounty, etc.)
Exposure to AI/LLM-based systems
Familiarity with browser automation or large-scale distributed systems
Skills
Python
Go
Kubernetes
Playwright
Chromium
PostgreSQL
S3
Redis
Kafka
OpenTelemetry
Grafana Cloud
GitLab CI
Terraform
EKS
LLM
Benefits
Stock options
Health insurance
Meal vouchers
Gym membership
Learning budget
Equipment
Open source time
Conference attendance
Location: Paris (75002) | Hybrid (2 days remote/week)
About Escape
Join Escape, a fast-growing leader in AI-powered cybersecurity. Our mission-driven engineering team (21 engineers, 4 technical leads, 3 product owners) is dedicated to redefining application security with cutting-edge detection and automation.
We operate at the forefront of technology and research from our Paris office.
About the Role
As a Senior Research Engineer you will work in a team with talented security researchers. You’ll champion innovation, and ensure research translates into impactful production solutions.
What You’ll Do
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Agentic scanning systems: Build systems that autonomously explore applications, maintain context, and execute complex testing strategies.
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Security detection engine: Design and implement reliable, production-grade vulnerability detection logic (injection, auth issues, business logic flaws, etc.).
•
AI-driven testing: Integrate LLMs and structured reasoning into scanning workflows to improve coverage and reduce false positives.
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Distributed infrastructure: Work on systems that orchestrate thousands of concurrent scans (Kubernetes, queues, resource management).
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Performance & reliability: Ensure scans are fast, deterministic, and cost-efficient despite high complexity and concurrency.
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Core platform design: Contribute to architecture decisions around memory systems, execution environments, and data models.
Our Tech Stack & Focus
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Languages: Python (primary), Go (performance/network components)
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Runtime: Containerized workloads on Kubernetes (EKS), supporting long and resource-intensive scans
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Scaling: Kubernetes Jobs & queue-driven autoscaling