• Develop and Optimize Computer Vision Algorithms • Contribute to BEV and 3D Perception Architectures • Data Management and Processing • Model Deployment and Optimization • Cross-functional Collaboration • Research and Innovation • Leadership

Role Description As a Senior Machine Learning Engineer at FreshBooks, you will own the full machine learning lifecycle, from early experimentation and prototyping to deployment and monitoring in production. You will work on moderately complex problems, refine ML processes, and support teammates through mentorship and collaboration. You will help deliver ML-powered features while optimizing systems for reliability, performance, and rapid experimentation. This role is primarily focused on machine learning, with supporting software engineering skills to enable production deployment. This role is ideal for someone who enjoys taking ideas from concept to customer impact. NOTE: This role can be worked remotely from the above location. What You’ll Do - Design, prototype, and validate machine learning models for product features and internal tools - Own the ML lifecycle from experimentation through production deployment and monitoring - Partner with Data Engineering and Product Engineering to integrate models into batch and online systems - Build and maintain shared feature pipelines, including documentation and versioning - Write production-ready code with testing, observability, and monitoring - Design experiments (A/B test, pre/post analysis) and interpret results to guide product and business decisions - Improve ML reliability through retraining workflows, drift detection, and root cause analysis - Design and build end-to-end pipelines for classification, ranking, embeddings, or generation tasks Qualifications - 5+ years of experience in data science, applied ML, or ML engineering roles - Strong foundation in supervised and unsupervised learning, statistical modeling, and experimentation techniques - Experience developing and shipping ML models in production (batch or real-time) - Strong Python and SQL skills with experience working with structured and unstructured data - Hands-on experience building and deploying ML or LLM-based systems (e.g. retrieval-augmented generation, embeddings, prompt tuning) - Familiarity with cloud infrastructure and ML tools, ideally on Google Cloud Platform (e.g. Vertex AI, BigQuery, Cloud Composer, Kubernetes) - Experience working with CI/CD pipelines, containerization (Docker), and job orchestration tools (Airflow, dbt, etc.) - Strong communication skills and ability to explain technical concepts to non-technical stakeholders You’ll Stand Out If You Have - Experience working with agentic models - Familiarity with LLM orchestration frameworks (e.g., ADK, LangChain, Semantic Kernel, Haystack) - A background in software engineering (e.g., system design, APIs, or distributed systems)

• Optimize the training and inference performance of our deep learning models • Continuously oversee the performance of models in production and develop monitoring tools to gain deeper insights into areas for model performance enhancement • Reduce the training and inference time of our neural networks • Drive projects and lead new initiatives in MLOps

• Design, build, and ship production new machine learning models that power core product features on the Zillow app, website, and email/push notifications. • Re-architect our core home ranking and recommendation systems to support advanced neural networks and dramatically accelerate the pace of experimentation across surfaces. • Own the full lifecycle of your models, from offline experimentation and prototyping with massive datasets to online deployment, A/B testing, and performance monitoring. • Pioneer the application of cutting-edge deep learning and large language models (LLMs) to improve our home shopping experience. • Develop new AI components that optimize how we display and when we recommend homes, ensuring we connect shoppers with the right content on the right properties at the right time. • Collaborate in a cross-functional group of engineers, applied scientists, product managers, and designers to define, execute, and iterate on the team's strategic roadmap. • Contribute to the team's engineering excellence by improving our machine learning infrastructure, development standards, and shared tooling. • Act as a key technical voice, mentoring other engineers and helping to shape the long-term vision for artificial intelligence in the home shopping experience.