Senior Machine Learning Engineer

at  TrueML

WHY TRUEML?

TrueML is a mission-driven financial software company that aims to create better customer experiences for distressed borrowers. Consumers today want personal, digital-first experiences that align with their lifestyles, especially when it comes to managing finances. TrueML’s approach uses machine learning to engage each customer digitally and adjust strategies in real time in response to their interactions.
The TrueML team includes inspired data scientists, financial services industry experts and customer experience fanatics building technology to serve people in a way that recognizes their unique needs and preferences as human beings and endeavoring toward ensuring nobody gets locked out of the financial system.

BENEFITS, PERKS, AND CULTURE

• Everything you need to work remotely
• Unlimited PTO
• Medical/dental/vision insurance

• 401k through Charles Schwab

• Flexible Spending Account, Limited FSA, and Health Savings Account- with an eligible health care package.

• Company-paid short-term and long-term disability plus basic life insurance.

• Family-friendly maternity and paternity leave
• Employee assistance program (EAP) via Claremont. Get free short-term counseling for mental health, free + discounted legal consultations, free financial consultations, access to work/life consultants, and more!
• PerkSpot discount program. PerkSpot offers exclusive discounts to 900+ merchants nationwide, and has exclusive discounts up to 60% on hotels worldwide.
• Paid time off to do volunteer work in your community.
• Access to the Wellness Coach app for you and 5 family members

We are a dynamic group of people who are subject matter experts with a passion for change. Our teams are crafting solutions to big problems every day. If you’re looking for an opportunity to do impactful work, join TrueML and make a difference.

YOUR ROLE:

You will join our Data and ML Engineering team, where we’re building a data-driven culture and revolutionizing how we help our users. Our machine learning engineers develop and own mission-critical ML models that serve millions of customers daily through our platform.
We are seeking an exceptional Senior Machine Learning Engineer who combines deep ML expertise with strong data engineering fundamentals. In this role, you’ll architect and implement our ML infrastructure, develop production-grade ML pipelines, design robust data ETL processes, and maintain high-performance systems that power both real-time and batch decision-making at scale. The ideal candidate brings hands-on experience in productionizing ML models, optimizing data pipelines, and a proven track record of delivering large-scale ML solutions that drive measurable business impact.

KEY RESPONSIBILITIES

• Building ML Infrastructure: As the main architect, developer, and owner of the Machine Learning infrastructure in production, your role will be to design, architect and build a scalable and efficient infrastructure that serves our needs.
• ML Pipeline Development: Our vibrant data science team develops new models that solve business problems. The role of our Senior MLE is to understand each model, finding solutions to scale them, and deploy the required pipeline for them.
• Architecting Data Platform: As a part of the Data and ML Engineering team, you will work closely with other Data Engineers to find the best solutions for building scalable data platform and supporting existing pipelines.
• Feature Engineering: Creating and maintaining offline and online feature stores and developing requires features for each model.
• ML Infrastructure Development: Making a scalable, modern and efficient infrastructure for ML models is one of the main responsibilities of this role.
• Model Monitoring and Maintenance: We have a few models in production that require support and monitoring.
• Data Strategy: Participating in data engineering team strategy decisions is a key responsibility of this role.
• Collaboration: The Senior Machine Learning Engineer will help the data engineering team in making architectural and decision decisions that enables creating robust data and ML products, developing ETLs, and working closely with the Data Science team to scale their algorithms and deploy their models in production.