Machine Learning Engineer II

Lessen•Chicago, IL

73d•$108,000 - $136,000•Hybrid

About The Position

The Machine Learning Engineer II role is part of the Technology Team, which is responsible for providing industry-leading machine learning-based tools or processes to the Company, which provide a competitive advantage and efficiency. You will work closely with cross-functional teams to build intelligent systems that solve real-world problems using machine learning, deep learning, and data science techniques. This is a hybrid position, Tuesday and Thursday in the office.

Requirements

PhD or Master’s degree in Math, Physics, Statistics, Computer Science, or other related fields
1-3 years of experience as a data scientist, working closely within a business
Strong mathematical and statistical skills
Programming Languages: SQL, Python
Understanding of ETL tools and database architecture
Familiarity with pytorch, pandas and cloud platform like AWS, Azure and GCP
Prompt engineering experience
Experience using a machine learning framework e.g. scikit-learn, pytorch or similar
Experience with a statistical software (e.g. R, SAS)
Experience with natural language processing
Advanced knowledge of Data Warehousing and BI best practices
Experience with big data tools such as Spark, Hadoop, etc.
Experience with Tableau or Superset
Verbal Communication – Proficient
Written Communication – Proficient
Teamwork – Advanced
Relationships – Advanced
Organizational Awareness – Advanced
Learning Agility – Advanced
Analysis – Advanced
Problem Solving – Advanced
Process Orientation – Advanced
Prioritization – Advanced
Customer Service – Proficient
Process Orientation – Advanced
Negotiation - Advanced

Responsibilities

Design, deploy, fine-tune, and evaluate large language models (LLMs); develop effective prompting strategies to optimize performance for various business use cases.
Improve data quality by cleaning the company’s data and sourcing information from new data sources.
Use machine learning/ deep learning to enhance and automate processes in Lessen proprietary tools.
Translate business problems into scalable AI/ML solutions, particularly using LLMs, in areas such as copilot systems, intelligent voice assistants, and work order processing optimization.
Collaborate cross-functionally with teams to develop, refine, and scale data-driven procedures and workflows.
Foster a positive team environment.
Ensure confidentiality and integrity of internal and external data.
Perform ad-hoc projects and other duties as assigned.