Why This Job is Featured on The SaaS Jobs
This Machine Learning Engineer role sits at a mature SaaS intersection where security, privacy, and product experience converge. The remit focuses on turning web understanding research into production capabilities such as phishing detection and credential risk signals, which are increasingly central to subscription software that must earn trust continuously. The explicit “zero-knowledge architecture” framing also signals a SaaS environment where technical choices are constrained by privacy guarantees, not only model accuracy.
For a long-term SaaS career, the work offers a clear path from experimentation to shipped features. Building and maintaining data pipelines, deploying models, and iterating on real-world performance maps closely to how modern SaaS teams operationalise ML. Exposure to LLM-based applications, RAG, and tool-using agents further aligns with the direction many SaaS products are taking, where ML is embedded into workflows rather than isolated as research.
This role is best suited to an engineer who prefers rigorous, hypothesis-driven work but wants accountability for production outcomes. It will fit someone comfortable collaborating across engineering and product disciplines, and who values building ML systems that must function under privacy and security constraints in a user-facing SaaS surface.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the role:
You'll work on cutting-edge research in web understanding, using large labelled and unlabelled datasets of web pages. As a member of the ACE team, you'll help pioneer a state-of-the-art web analysis that will make life easier and more secured for millions of people worldwide. Its mission is to enhance user security and core product features through intelligent, AI-powered capabilities, such as improving the autofill experience, detecting credential risks, and protecting against phishing attacks. The team comprises Software Engineers, and Machine Learning Engineers, to ensure the quality of our analysis.
We leverage advanced Machine Learning techniques to build intelligent security tools within a zero-knowledge architecture. Our blog post demonstrates how we implement autofill capabilities without accessing user data. We also explain our privacy-preserving approach to real-time threat detection and phishing prevention.
We also advocate for public specifications for field autofill, like Semantically Annotated Web Forms.
Join our team and help us make a difference in the world!
Location:
You will be based in Paris, with a hybrid policy (3 office days / week) and English as your working language. At Dashlane, we embrace a hybrid culture that combines the best of both worlds: the creativity and energy of in-person collaboration with the flexibility of remote work. Our model is designed to strengthen team connections while supporting individual productivity and work-life balance. To maximize collaboration, we come together in the office on Mondays, Tuesdays, and Thursdays, while Wednesdays and Fridays offer more flexibility for focused work.
About our stack:
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Languages:
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Backend: NodeJS, Typescript
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Frontend / internal tooling: React, Typescript
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ML: Python
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ML: Scikit-learn, Optuna, Onnx
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MLOps: Dvc, Mlflow
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Versioning: Gitlab
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CI/CD: Gitlab, Sonarqube, Terraform
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AI : Claude Code
At Dashlane you will:
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Work on R&D initiatives around web pages understanding and their applications to a Credential Protection (password manager, identity provider, threat detection)
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Deploy, evaluate, and continuously improve AI-powered features and agentic systems in production, including LLM-based applications, retrieval augmented generation (RAG), tool-using agents, and workflow automation
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Help maintain and improve our existing data pipelines (data collection tools and databases, model training pipelines, integrations into the Dashlane web extension)
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Work with ML Engineers, Software Engineers, product managers, designers in a highly collaborative environment where everyone shares ownership of the company's success
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Act as an AI Pioneer within the engineering organisation, demonstrating proven experience integrating modern GenAI tooling into day-to-day workflows
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Learn every day, and share your knowledge with your coworkers
Requirements:
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A Master / Engineer degree in Machine Learning/AI or Computer Science
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4+ years of professional experience in product led company
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including 2+ years of experience in R&D
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Practical experience in Machine Learning based application
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Handling large amounts of data
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Python, ML frameworks (Scikit-learn XGBoost…) and deep learning frameworks (PyTorch, Hugging Face…)
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Scientific mindset and technical rigour
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Fluent in AI-assisted engineering
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Fluent in English (as the team is international)
Nice-to-have:
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Interest in JavaScript, Web technologies and embedding Machine Learning within web applications
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Experience integrating foundation models, LLM-powered features, retrieval-augmented generation (RAG), or AI agents into production.
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Experience with Machine Learning cloud platforms (Azure, AWS, GCP)
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Interest in data privacy and online security
What Dashlane offers you:
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Swile card
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Well-being days - 4 extra days (one per quarter) to acknowledge the importance of your wellbeing
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Mental health services through Spring Health for you and your family members
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Equal Parental leave - regardless of gender, up to 20 weeks fully paid leave to take care of their new baby, within the first year of birth or adoption
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Mentorship program - select your mentor from our internal pool and continue your learning path!
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Flexible working hours - depending on the role, determine a schedule that fits your need, in alignment with your manager
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Team building & seasonal social events
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Weekly company-sponsored lunch in the office (Tuesdays), monthly happy hour and much more
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More about our other benefits
