Staff AI/ML Applied Engineer

Esusu: Building Credit Access for All

Your financial future shouldn't depend on your race, background, or zip code. Yet 45 million Americans remain "credit invisible," while countless others face denials, evictions, and predatory interest rates regularly. Esusu is changing that.

As a financial technology and data platform, we're revolutionizing financial health, identity services, and inclusive decision-making for individuals and businesses alike.

Our impact speaks volumes:

• $50 billion in credit activity facilitated
• 5 million+ rental units in our network
• Trusted partnerships with leading real estate companies, government entities, fintechs, banks, and employers

Through our enterprise services, embedded products, and consumer offerings, we're creating a financial system that works for everyone—right when fintech innovation matters most. Join us in building a future where financial systems are inclusive by design, access is standard, and opportunity reaches everyone. Unlock Tomorrow with Esusu.

The Engineering Team 

We are building a team of owners and changemakers. It is not enough to have the intention to do good –Esusu engineers translate intent into action and outcomes. We believe that lone heroes cannot accomplish our ambitious goals. Lasting change requires work by determined and collaborative individuals with a customer-first mindset. Our engineers aim to delight our customers and drive our business and team forward by leveraging their technical skills, empathetic curiosity, and teamwork.

Our back-end is microservice-based, hosted in AWS, and mainly written in Go. Our front end is based on React and written in Typescript. Our mobile apps are built using Flutter. Our data infrastructure is based on MongoDB, PostgreSQL, S3, Sagemaker, Snowflake, Talend, and mainly written in Python.

We are continuously innovating and always open to new solutions. Because Esusu is a financial services company, we are obsessive about every aspect of compliance and security. We embrace a DevOps environment where the team is responsible for the full spectrum of development, testing, deployment, and maintenance, including working with product and business operations teams to create customer-focused solutions. Even though we are 100% remote, Esusu’s culture is intensely collaborative.

The Opportunity

Esusu is seeking a ML Engineer to apply 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 work closely with the Data Analytics and Data Engineering teams to ensure the models are trained on high-quality data and integrated into production systems. This role requires a high degree of collaboration with key managers, product owners, and other peers and cross-functional partners that rely, produce, and interact with the data domain across the organization (e.g., Data Engineers, Data Analysts, Backend Engineers, Marketing, Sales). The ML Engineer will focus on creating interpretable, accurate, and scalable predictive models utilizing datasets generated from our AWS and Snowflake environments. Additionally, you will translate model insights into actionable strategies that drive business decisions and financial inclusion. 

In addition to the technical responsibilities, you will coach and mentor your fellow data analysts and data engineers. You will ensure efficient delivery through effective planning, engaging with others, prioritizing, and developing, testing, and releasing your work. You will exhibit and foster Esusu’s culture and operating principles.

Core Competencies

• Superb programming, software and data development skills – You can independently devise and implement solutions to problems with minimal explanation needed.
• Strong communication skills – You can efficiently translate between technical and non-technical audiences and have strong writing skills.
• High standards – Your work is of the highest quality and you continue to raise the bar within your immediate team and our organization.
• Balance velocity with long-term goals – You balance thinking big with delivering the right thing in an agile and speedy manner. You are curious, flexible, and nimble in your approach and implementation.
• Heart of a teacher – You are a capable mentor and able to inspire and empower others on your team.
• Getting work done and driving excellence – You strive for excellence and prioritize delivering high-quality outcomes and projects..
• Adaptability – You thrive in an environment that changes quickly and is constantly presenting new challenges. 

Basic Qualifications

• Knowledge & Background: Master’s degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis. We will also consider candidates with a relevant bachelor’s degree in a STEM field with 10+ years of relevant work experience in Machine Learning and Statistics.
• AI & ML Experience: Very 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 strong understanding of the theory and tradeoffs behind these techniques.
• AI Modeling: Proficiency in statistical and machine learning techniques for predictive modeling, classification, and regression.
• Programming Languages: Very strong knowledge of Python and SQL.
• Cloud Expertise: Strong experience with AWS cloud services and tools, including AWS SageMaker for model development, training, and deployment, and AWS Bedrock for building and fine-tuning foundation models.
• AI & ML Platforms: Experience in working with model registry tools such as MLflow, SageMaker Model Registry, or other similar systems, to track, version, and manage machine learning models throughout their lifecycle.
• DataOp & 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 with agent-based frameworks such as 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 & Background: PhD degree in mathematics, statistics, economics, computer science, or other quantitative disciplines with a focus on data analysis.
• FinTech/PropTech Expertise: Experience working in FinTech or PropTech sectors.
• Startup Experience: Familiarity with the dynamics of startup environments.
• Software Engineering Experience: Experience with microservices.
• Compliance Knowledge: Experience working in SOC2-certified, ISO-certified, or similar organizations.

Key Technologies We Use

• Backend: Go, Python.
• Cloud: Amazon Web Services (AWS).
• Data: MongoDB, PostgreSQL, S3, Sagemaker, Snowflake, Tableau.
• Front End: React, Typescript.
• Mobile: Flutter, Dart.

Benefits

Remote first work environment - Where we trust you to get your work done.
Generous PTO – Enjoy a generous time off policy and 14 company wide paid holidays.
Premium Health Coverage – Comprehensive medical, dental, and vision insurance.
Partial-Employer Funded Health Savings Account - Available for High Deductible Plans.
Paid Parental Leave – 12 weeks of 100% pay to support growing families.
Retirement Plans – Secure your future with our competitive 401(k) offerings, including a 4% company match (for US only).
Tuition Reimbursement – For qualified programs to complement work experience.
Professional Development Stipend – To support your continued growth and learning.
In person and virtual team get togethers - To connect with your fellow Esusu colleagues.
Free Credit Building Benefit - Build your credit score for free as an Esusu employee.

This job is eligible only for the following states: California, Colorado, District of Columbia, Georgia, New York, Illinois, Washington