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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - Embedded AI - **Company:** PENNYLANE AFFILIATES LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Monitoring of Systems, Python (Programming Language), Machine Learning, Microsoft Copilot, Pytorch, Deep Learning, Pyspark, Deployment Automation, Embedded AI, Machine Learning Operations, Human in the Loop, Amazon Redshift - **Published:** October 3, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pou6axtmqo ## About the Role * 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. Nice to have: * 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. What does the recruitment process look like? * 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) ## Description By joining us as a Machine Learning Engineer - Embedded AI, you will turn difficult accounting problems into trusted ML products used every day. You will combine strong ML engineering, product thinking and production ownership to improve both automation and user trust. HOW you will contribute as a Machine Learning Engineer - Embedded AI 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. 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. 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. And beyond: the ML & AI teams will continue growing with the company This means opportunities to mentor new team members, lead major product initiatives and help define the technical standards that make trusted AI possible at Pennylane. ## Related Videos - [AI for decision-making in Tech Recruiting](https://www.wearedevelopers.com/videos/1074-ai-for-decision-making-in-tech-recruiting) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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