AI Engineer, Emburse

• BS in Statistics, Mathematics, Computer Science or another quantitative field with at least 6 years
• Experience manipulating data sets and building GLM/regression models, ensemble decision trees and neural networks
• The ideal candidate will have demonstrated hands-on experience with foundation models, GenAI and agents over the last 1-2 years. Graduate degree preferred
• Strong problem solving skills with an emphasis on product development
• 6+ years of experience developing data science products
• Strong experience using and optimizing common python machine and deep learning libraries such as Scikit learn, PyTorch, TensorFlow, Keras, MXNet and Spark MLlib
• Experience using statistical computer languages (Python, R, Scala, SQL, etc.) to manipulate data and draw insights from large data sets
• Hands-on generative AI development Experience using foundation models (LLMs, VLMs)
• Experience with model fine tuning of open source foundation models with proprietary data
• Experience leveraging AI metrics for monitoring and value tracking
• Knowledge of AI Agent frameworks with recent hands-on experience building an AI agent able to autonomously use data stores, tools and other AI models to solve inquiries
• Deep knowledge of data science concepts and related product development lifecycle
• Experience using machine learning libraries such as TensorFlow, Keras, SparkML etc
• Working knowledge of machine learning tuning optimization procedures
• Experience working with and creating data architectures
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
• Excellent written and verbal communication skills for coordinating across teams
• A drive to learn and master new technologies and techniques

Desirable

• As an AI Engineer at Emburse, you will drive design and implementation of artificial intelligence inference applications and products using a variety of approaches including foundation models (GenAI), agents, deep learning, neural networks and other machine learning techniques to help build the future of a completely automated invoice and expense management lifecycle
• As a key member of our team, you will be responsible for designing, developing, and deploying AI solutions that further automate expense and invoice management processes and solve complex optimization problems
• By combining expertise in machine learning, deep learning, and data science; you will create systems capable of learning and adapting over time leveraging production feedback mechanisms
• Collaborating with cross-functional teams, the Emburse AI engineer will build and train machine learning models, develop algorithms, and integrate AI systems into existing software and infrastructure
• In addition to technical responsibilities, the Emburse AI engineer will play a strategic role by identifying opportunities for AI applications and driving innovation across the enterprise
• You will work closely with data scientists, software engineers, and product stakeholders to ensure AI models align with organizational goals and deliver value
• With a focus on continuous improvement, you will need to stay updated on the rapidly emerging advancements in artificial intelligence, exploring new tools for finetuning, distilling and deploying open-source foundation models
• Further, you will be on the forefront of widening the scope of intelligence of Emburse AI by leveraging agents to retrieve canonical knowledge and make autonomous inferences for the purpose of handling user inquiries
• This dynamic role is essential for Emburse to remain competitive in a rapidly evolving technological landscape
• Able to translate product design requirements into pipeline of heuristic and stochastic algorithms
• Understand exploratory data analysis for product feasibility studies and ground truth testing
• Execute SQL queries and/or python scripts to manipulate, analyze and visualize data
• Able to implement explainable AI solutions and rationalize model inferences
• Follows SDLC processes, Adopt agile-based processes/meetings and peer code-reviews
• Works with Machine learning engineer/architect to deploy data products into production
• Follows and understands legal data use restrictions
• Contributes to algorithm library development and design for ML, NLP and XAI
• Delivers product pipelines for deployment to production
• Builds applications that integrate third party and self-hosted foundation models
• Fine tunes open source foundation models (LLMs, VLMs) with proprietary data
• Develops autonomous AI inference and tool use orchestration using ReAct AI agents
• Provides root cause analysis for machine learning model inference
• Completes data analysis or processing tasks as directed
• Documents data product end to end design and development
• Data annotation, labeling and other related data generation activities
• Provides thought leadership for rest of team and seeks out opportunities to mentor more junior team members
• Presents and holds data product updates and trainings
• Updates team on data product performance