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Senior Machine Learning Engineer - Embedded AIPennylane · All France (remote)
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Pennylane

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Senior Machine Learning Engineer - Embedded AI

🗓️ Posted 2026-09-09 All France (remote) Fulltime Hybrid ICT
💰 Salary Not shared 📊 Market 5,000 EUR–7,500 EUR/mo

About the Company

Pennylane is one of the fastest growing Fintechs in France (and soon in Europe ). In 5 years of existence, we've managed to: - Make ourselves known as a groundbreaking accounting and financial software for small businesses and their accountants - Raise a total of 400 million, including from Sequoia - the famous Silicon Valley fund that invested early in companies like Google, Facebook, Airbnb, Stripe and Paypal - Grow from 7 cofounders to 1,000 happy Pennylaners, and earn a place among the greatest companies to work for in France (and remotely), with a 4.6/5 rating on Glassdoor - Build an international environment with more than 25 nationalities and a strong remote-friendly culture, where 30% of employees already work from all parts of Europe - Earn the trust of thousands of customers and accounting firms, with outstanding ratings - Reach more than 1,000,000 small and medium-sized enterprises (SMEs) and over 6,000 accounting firms using Pennylane in France WHY THIS POSITION IS OF UTMOST IMPORTANCE TO REACH OUR MISSION Pennylane is becoming the financial OS of European SMEs and accounting firms. A growing share of the work done inside our product will be automated or assisted by AI. Our strategy operates at three layers: verticalized AI embedded in the product, an agent interface that makes Pennylane readable and actionable by AIs, and the Pennylane orchestrator agent itself. The Embedded AI team owns the first layer. We build the specialized

Responsibilities

  • ● ks closely with product squads and accounting experts, from initial exploration to production, monitoring and continuous improvement.
  • ● You will design and ship ML systems for document understanding, extraction, classification, matching, ranking, scoring and recommendations.
  • ● You will contribute directly to our Copilot and Autopilot experiences, including Bookkeeping Autopilot and Revision Autopilot.
  • ● You will own the full lifecycle of your solutions: problem framing, data and labeling strategy, baselines, training, evaluation, deployment, experimentation, monitoring and maintenance.
  • ● You will turn user corrections and production failures into better datasets, models and product behavior.
  • ● You will define quality metrics that reflect real user value: precision and recall, automation coverage, straight-through processing, human correction rate, latency and cost.
  • ● You will partner with Product, Engineering and accounting experts to understand workflows, define what "correct" means and integrate ML naturally into the user experience.
  • ● You will choose the simplest reliable approach for each problem - deterministic logic, classical ML, deep learning or generative AI - rather than starting from a preferred model.
  • ● You will help improve our shared ML engineering practices: reusable components, experimentation, observability, data quality and reliable training and inference pipelines.
  • ● You will stay ahead of the curve by monitoring emerging ML and AI techniques - including multimodal and generative models - and applying them when they create measurable value.
  • ● WHAT YOU CAN EXPECT FROM YOUR LIFE AT PENNYLANE
  • ● Within one month:
  • ● You will learn about Pennylane, our users, our accounting workflows and our AI vision during onboarding.
  • ● You will get familiar with our ML stack, production systems, datasets, metrics and ways of working.
  • ● You will meet your product and engineering partners and contribute to a first scoped improvement.
  • ● Within 3 months:
  • ● You will own an Embedded AI use case end to end, with clear offline and production metrics.
  • ● You will have shipped a meaningful improvement to a Copilot or Autopilot capability.
  • ● You will be comfortable with our technical stack, including Python, Pytorch, PySpark, Redshift, Airflow, AWS SageMaker and our monitoring tools.
  • ● You will use real user feedback and error analysis to prioritize the next iterations.