AI engineer

About Symbiotic Security

➡️ Morning Laffitte (Paris 9ᵗʰ)

The role – Senior AI Engineer

What you will work on

• Design and own AI features end‑to‑end that protect code generated by humans and by AI tools (Copilot, etc.), from ideation and prototyping to production and iteration.
• Define and drive our data strategy for AI: dataset definition, synthetic data generation, labelling strategies, and quality control.
• Build and improve evaluation and testing pipelines for our AI assistant so that we can systematically measure quality, regressions, and business impact.
• Prototype fast and industrialise selectively: explore multiple ideas, select the most promising ones, and drive them into production with the engineering team.
• Shape our AI architecture and tooling (prompting strategies, retrieval approaches, memory mechanisms, observability, evaluation frameworks).
• Partner closely with Product, security researchers, and engineering leads to align AI initiatives with user needs and company priorities.
• Mentor more junior engineers on AI topics and contribute to structuring our AI practice (best practices, documentation, standards).

What success looks like in your first year

• You have a deep understanding of our product, users, and AI stack (Bedrock, LangChain, internal tooling, experimentation environment).
• You have delivered at least one PoC of an AI‑powered protection feature and significantly improved an existing AI component.
• You are seen as a go‑to person on AI ideas and trade‑offs.

• You have industrialised one or more PoCs end‑to‑end with the engineering team, from prototype to production.
• You have set up or redesigned key evaluation pipelines and quality metrics for our AI assistant.
• You are a core partner for Product and the CTO on AI roadmap decisions.

• You are a reference point for AI topics in the company and drive complex, cross‑functional projects with high autonomy.
• You have materially improved our time‑to‑market on AI protection ideas and our overall AI quality.
• You actively mentor others, spread good practices, and help make Symbiotic a top‑tier place to do applied AI for security.

Tech Stack & Environment

• Backend: Python 3.12+ / Django (monolith, REST API)
• Frontend: TypeScript / React (SPA) for the frontend of the SaaS platform
• TypeScript for the IDE plugin
• AI stack: AWS Bedrock, LangChain, LLM proxy, internal observability to monitor latency and quality of AI calls
• Database: PostgreSQL
• Architecture: Monolithic, Hexagonal architecture, DDD being introduced
• Cloud: AWS
• Tests: 94% backend coverage, 100% business logic covered
• Tools: Sentry, logs, traces, internal analytics
• Satellite services: CLI tools & AI agents written in Python

Your future team

• Edouard (CTO)
• Salah Eddine (AI engineer)
• Abir & Minh Thang (Engineering Manager / Tech Lead)
• Alexandre, Lucine, Koceila (fullstack software engineers)
• Matthieu (SRE) and Anthony (cybersecurity researcher)
• Alexis (Product Manager) and Quentin (Product Designer)

How we work

• 2‑week cycles, dailies and retros
• Short briefs & kick‑offs
• High availability of the EM/CTO for decisions
• Strong expectation of ownership, even for junior profiles, with support and mentoring

• Curiosity (especially around AI and new tech)
• Product thinking (understanding the “why”, not just executing the “how”)
• Clear communication on blockers and progress
• Comfort with ambiguity and evolving priorities

What we’re looking for

• Strong, hands‑on experience with LLMs in production: you have already built and shipped RAG systems or similar LLM applications with real users.
• Solid software engineering background, ideally in Python, with the ability to own complex systems (architecture, performance, reliability).
• Proven ability to design experiments, choose the right metrics, and iterate systematically.
• Experience with modern AI tooling (e.g. Bedrock, LangChain or similar frameworks, evaluation stacks, observability for AI systems).
• Comfortable working close to the product: clarifying use cases, challenging requirements, and balancing UX, performance, and cost.

• Experience with security, developer tools, or infrastructure / platform products.
• Exposure to model fine‑tuning or non‑LLM ML (e.g. ranking, anomaly detection, embeddings, classical ML).
• Experience designing or operating high‑leverage internal platforms (evaluation infra, experimentation frameworks, data pipelines).
• Passionate, has worked with Small Language model, will help the engineering team to evolve.
• Background in a mature AI environment
• Strong scientific background (top engineering school or equivalent in maths / CS / applied physics).

• Strong ownership and leadership: you identify problems, propose solutions, and drive them to completion, often across teams.
• Low‑ego, high‑standards attitude: you care about impact and quality more than about being right.
• Ability to mentor and uplevel others, both on AI and engineering topics.
• Comfortable working in an environment with high expectations, fast iteration, and evolving priorities.

Why join Symbiotic as a Senior AI Engineer?

• Huge leverage in a small AI team (~2–3 people) rather than being a specialist in a large, siloed AI organisation.
• A very multi‑disciplinary role spanning product, engineering, and security instead of owning a tiny “feature vertical”.
• A diverse technical environment (security, backend, AI, SRE, product) where you can have real breadth.
• Ideal if you want more substance and scope than a hyper‑narrow expert role.
• Opportunity to shape how we build a scalable cybersecurity SaaS powered by AI.
• 🪴 Strong career and leadership opportunities by joining early and helping define our AI practice.
• 🧑‍🔬 A strong product & engineering culture with high technical ambitions.
• 🥂 A vibrant, inclusive culture with regular team events and offsites.
• ☝🏽 Direct, visible impact on the product and on Symbiotic’s trajectory.

Compensation & Benefits (Senior)

Hiring process

• Call with our Talent Acquisition – Blanche – 30 minutes
• Meeting with our CTO – 45 minutes to 1 hour
• Case study to be done at home (async work)
• On‑site session with our AI / engineering team (code review and discussion) – ~1.5 hour
• Culture fit – lunch with the team – 2 hours
• Formal offer