Senior Machine Learning Engineer - Embedded AI

Pennylane SAS

12d•Remote

About The Position

Pennylane is seeking a Senior Machine Learning Engineer specializing in Embedded AI to join their growing ML & AI organization. This role is crucial for developing specialized machine learning systems that power Pennylane's Copilot and Autopilot features, focusing on areas like invoice parsing, document classification, accounting suggestions, matching, and scoring. The engineer will be responsible for turning complex accounting problems into reliable ML products, combining strong ML engineering skills with product thinking and production ownership to enhance automation and user trust. The position involves working closely with product squads and accounting experts throughout the entire ML lifecycle, from problem framing and data strategy to deployment, monitoring, and continuous improvement.

Requirements

5-8 years of experience.
Very strong in Python.
Hands-on experience building and operating machine learning systems at scale in production.
Ability to frame an ambiguous product problem, establish a baseline, and select useful metrics before optimizing a model.
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.
Balanced blend of technical, business, and product skills.
Ability to communicate well with software engineers, product managers, and non-technical domain experts.
Fluent in English.

Nice To Haves

Experience with accounting, fintech, or other high-trust business workflows.
Experience with human-in-the-loop systems, active learning, or learning from user corrections.
Experience running ML systems at scale with strict latency, reliability, or cost constraints.
Familiarity with multimodal or generative models for document understanding.

Responsibilities

Design and ship ML systems for document understanding, extraction, classification, matching, ranking, and recommendations.
Contribute directly to Copilot and Autopilot experiences, including Bookkeeping Autopilot and Revision Autopilot.
Own the full lifecycle of ML solutions: problem framing, data and labeling strategy, baselines, training, evaluation, deployment, experimentation, monitoring, and maintenance.
Translate user corrections and production failures into improved datasets, models, and product behavior.
Define quality metrics that reflect real user value, such as precision, recall, automation coverage, straight-through processing, human correction rate, latency, and cost.
Partner with Product, Engineering, and accounting experts to understand workflows, define correctness, and integrate ML seamlessly into the user experience.
Select the simplest reliable approach for each problem (deterministic logic, classical ML, deep learning, or generative AI).
Improve shared ML engineering practices, including reusable components, experimentation, observability, data quality, and reliable training/inference pipelines.
Stay updated on emerging ML and AI techniques, including multimodal and generative models, and apply them where they provide measurable value.