Machine Learning Engineer
The Opportunity:
We are seeking a Machine Learning Engineer to develop and deploy advanced machine learning models that predict and model creditworthiness, transaction labeling, identity mapping, underwriting, cash flow, lease renewals, and other key financial factors. This role will collaborate closely with the Data Analytics and Data Engineering teams to ensure high-quality data is integrated into production systems.
This position requires collaboration with key stakeholders, including Data Engineers, Data Analysts, Backend Engineers, Marketing, and Sales professionals. The Machine Learning Engineer will focus on creating interpretable, accurate, and scalable predictive models using datasets generated from AWS and Snowflake environments.
Core Competencies:
* Programming and Development Skills: Proficiency in devising and implementing solutions to problems with minimal explanation needed.
* Communication Skills: Ability to translate technical concepts to non-technical audiences and possess strong writing skills.
* High Standards: Delivering work of the highest quality and continually raising the bar within your team and organization.
* Balance Velocity with Long-term Goals: Balancing big-picture thinking with delivering the right thing in an agile manner.
* Mentorship and Empowerment: Inspiring and empowering others on your team.
* Excellence and Prioritization: Striving for excellence and prioritizing high-quality outcomes and projects.
* Adaptability: Thriving in a dynamic environment with new challenges.
Basic Qualifications:
* Education: Master's degree in mathematics, statistics, economics, computer science, or a quantitative field with a focus on data analysis. Relevant bachelor's degrees with 10+ years of experience may also be considered.
* Ai & ML Experience: Strong experience in AI and ML in the financial technology or service industry, including working with credit and financial datasets.
* Ai & ML Engineering: Proven track record in building and deploying machine learning models with a deep understanding of the underlying theory and trade-offs.
* Ai Modeling: Proficiency in statistical and machine learning techniques for predictive modeling, classification, and regression.
* Programming Languages: Strong knowledge of Python and SQL.
* Cloud Expertise: Experience with AWS cloud services and tools, including SageMaker for model development, training, and deployment, and Bedrock for building and fine-tuning foundation models.
* Ai & ML Platforms: Familiarity with model registry tools such as MLflow, SageMaker Model Registry, or similar systems for tracking, versioning, and managing machine learning models.
* DataOps & MLOps: Experience implementing DataOps, MLOps, and/or DevSecOps in the AI, ML, and software development lifecycle.
* ML Languages: Experience building ML models with PyTorch, Scikit-learn, and GenAI models.
* Llm Models: Experience working with LLM frameworks such as HuggingFace libraries and agent-based frameworks like LangChain and Mirascope.
* Data Experience: Familiarity with Snowflake for cloud data warehousing, data integration, and efficient handling of large-scale data storage and processing.
Above and Beyond:
* Specialized Knowledge: PhD degree in mathematics, statistics, economics, computer science, or a quantitative field with a focus on data analysis.
* FinTech/PropTech Expertise: Experience working in FinTech or PropTech sectors.
* Startup Experience: Familiarity with startup environments.
* Software Engineering Experience: Experience with microservices.
* Compliance Knowledge: Experience working in SOC2-certified, ISO-certified, or similar organizations.
Benefits:
* Competitive Salary: For a Series B startup.
* Remote Work Environment: Where we trust you to get your work done.