Machine Learning Engineer

Bardeen•San Francisco, CA

1d•Remote

About The Position

Responsible for developing next-generation AI systems designed to simplify task automation for users. This role involves designing, evaluating, deploying, and maintaining AI solutions, utilizing both Large Language Models (LLMs) and Bardeen's custom models in areas such as semantic parsing, dialog systems, agents, and text generation. The position collaborates with engineers to integrate AI features into Bardeen's products, ensuring a high-quality user experience.

Requirements

Master’s degree in Computational Science and Engineering, or a closely related field that focuses on Machine Learning
1 year of experience
Experience with modern deep learning models, particularly large language models (LLMs) and multimodal architectures used for understanding text, structured data, and behavioral traces.
Familiarity with OpenAI, Anthropic, or Hugging Face Transformers (GPT, Mistral, LLaMA, etc.).
Experience with Python, Hugging Face, and OpenAI, Gemini and Anthropic SDKs.
Experience with designing evaluation frameworks, benchmarking model variants, and measuring before/after impact.
Experience with production-grade data and inference infrastructure, including AWS and GCP.
Experience with monitoring, optimization, and scaling of LLM inference workloads across distributed systems.
Experience with ML and AI algorithms to model real world business processes and identification of high impact automation and AI agent opportunities.
Experience with using LLMs for performing statistical analysis.

Responsibilities

Research, design, and implement machine learning algorithms to optimize workflow automation.
Develop, test, and modify computer programs to apply machine learning models to real-world applications.
Research, design, and implement machine learning and AI algorithms to model real world processes, including process discovery, process conformance, and opportunity identification for automation and AI agents.
Develop, test, and modify computer programs that apply machine learning models to operational data sources such as event logs, clickstreams, tickets, documents, and call transcripts.
Design and improve methods for process and entity extraction from unstructured and semi structured data, including tasks, systems, stakeholders, and key business objects.
Stay familiar with and evaluate state of the art research in process mining, workflow intelligence, representation learning for events and processes, and LLM based planning and tool use, and translate it into practical enterprise solutions.
Perform statistical analysis and apply data mining techniques to diagnose bottlenecks, measure impact, and improve model performance and robustness in production settings.
Deploy machine learning models into production systems, ensuring scalability and efficiency.
Maintain, monitor, and enhance deployed machine learning systems to ensure continuous improvement.
Collaborate with software engineers, data scientists, and product teams to integrate AI solutions.
Prepare technical documentation and reports detailing methodologies and outcomes.
Utilize cloud computing platforms such as AWS and GCP to manage large-scale data processing and storage.
Ensure compliance with industry standards, data governance, and security protocols for machine learning applications.