Senior Machine Learning Engineer, Pennylane

• Have 5–8 years of experience and are very strong in Python
• Have hands-on experience building and operating machine learning systems at scale in production — not only training models or running notebooks
• Know how to frame an ambiguous product problem, establish a baseline and select useful metrics before optimizing a model
• Have strong experience in several of these areas: document AI, NLP, classification, ranking, recommendation, anomaly detection, deep learning or generative AI
• Care about data quality, observability, failure modes, cost and long-term maintainability as much as model performance
• Have a balanced blend of technical, business and product skills, and communicate well with software engineers, product managers and non-technical domain experts
• Are fluent in English; French is not mandatory

Desirable

• You will join the Embedded AI team within our ML & AI organization. The team works 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
• 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
• Within 6 months:
• You will lead larger cross-team ML projects and help shape the Embedded AI roadmap
• You will improve the reliability, automation coverage and maintainability of one or more production systems
• You will share best practices and raise the bar for ML engineering, evaluation and production ownership across the team

• A first interview with our Talent Acquisition Manager
• A case study interview covering problem framing, data, modeling, evaluation, deployment and monitoring (75 min)
• A past project interview to discuss your experience and technical decisions (60 min)
• An interview with our Tech & Product leaders to discuss our company culture (30 min)