PURPOSE OF THE JOBThe Machine Learning Engineer (MLE) selected for this role at ICW Group will build and deploy high-performing machine learning models across the enterprise. As an MLE on the rapidly growing Enterprise Analytics team, you will have the opportunity to shape the processes through which we build, deploy, and monitor models. This impactful role will put analytic applications into production that drive revenue growth, reduce expenses, and enhance the customer experience.
The insurance industry is full of complexity and is data-rich. You will collaborate closely with business partners across the enterprise in Underwriting, Claims, Finance, Actuarial, HR, Legal, and Technology to help us realize value from our extensive data resources.
ESSENTIAL DUTIES AND RESPONSIBILITIES- Build agentic and GenAI solutions to expand the use of AI throughout the organization.
- Collaborate with data scientists to make ML models production-ready and move them from the lab into live production environments.
- Help design and maintain engineering and model delivery standards.
- Create demos as well as full-featured applications.
- Put effective model monitoring and guardrails in place.
- Determine suitable AI techniques to solve stakeholder business problems.
- Collaborate effectively with data engineering and MLOps teams and recommend development patterns and process improvements.
- Ensure that internal and external data pipelines are constructed to support sophisticated data models and products.
- Work closely with IT Security and Data Governance to ensure that analytics practices meet data security, privacy, and quality policies.
- Contribute to the development of internal policies and ensure the Enterprise Analytics team is compliant with applicable regulations.
- Be willing to be in-office 3+ days per week at our sunny San Diego HQ.
- Have an interest in joining a company with a history of philanthropy and giving back, stability with over 50 years in business, flexibility with mindfulness of work-life balance, and a commitment to helping and investing in colleagues so they may become the best version of themselves.
SUPERVISORY RESPONSIBILITIESThis role does not have supervisory responsibilities.
EDUCATION AND EXPERIENCEBachelor's degree from a four-year college or university required, with a major in Computer Science, Electrical Engineering, or a related field, and a minimum of 3-6 years of industry experience in MLE roles; or an MS, MEng, or PhD in a related field with a minimum of 1-2 years of industry experience working as an MLE or in a closely related role.
CERTIFICATES, LICENSES, REGISTRATIONSNone required.
KNOWLEDGE AND SKILLS- A track record of putting ML models into production and deploying models at scale.
- Extensive cloud computing experience using AWS.
- Proficiency with databases and database methodologies, especially Snowflake.
- Strong knowledge and experience with containers, software design patterns, unit testing, CI/CD, microservices, creating REST APIs, and agile methodologies.
- Knowledge of and experience with LLMs and generative AI, as well as some of the following: statistical and machine learning algorithms, NLP, forecasting, recommender systems, reinforcement learning, deep learning, and optimization.
- Experience with Python, PyTorch, and Linux.
PHYSICAL REQUIREMENTSOffice environment - no specific or unusual physical or environmental demands. Employees are regularly required to sit, walk, stand, talk, and hear.
WORK ENVIRONMENTThis position operates in an office environment and requires frequent use of a computer, telephone, copier, and other standard office equipment.
This description is a general statement of essential job functions and responsibilities. The position may include other duties as assigned.
Relocation offered.
We are currently not offering employment sponsorship for this opportunity. #LI-TP1 #LI-HYBRIDThe current range for this position is
$105,780.03 - $189,347.93
This range is exclusive of fringe benefits and potential bonuses. If hired at ICW Group, your final base salary compensation will be determined by factors unique to each candidate, including experience, education and the location of the role and considers employees performing substantially similar work.
