Sr. Machine Learning Engineer (copy)

TrueML

31d•$164,000 - $194,000

About The Position

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. TrueMLâs 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. As our Senior Machine Learning Engineer, you will be the primary architect and visionary for the infrastructure that powers our data-driven culture. We are looking for a powerhouse who can bridge the gap between experimental Data Science and robust, production-grade engineering. Your mission is to build, scale, and own the mission-critical ML pipelines and data platforms that serve millions of our customers daily, turning raw data into real-time decisions.

Requirements

A Proven Builder: You have 5+ years of hands-on experience in ML Engineering, with a significant focus (3+ years) on the data engineering side of the house.
Cloud Native: You are an expert in the AWS ecosystem (Sagemaker, DynamoDB) and thrive using Infrastructure as Code tools like Terraform, CDK, or CloudFormation.
Automation Minded: You have a deep understanding of containerization and orchestration, specially using Docker and Kubernetes to deploy scalable workloads.
Technical Polymath: You possess a deep understanding of database systems, ETL architecture, and advance SQL, alongside mastery of Python and frameworks like TensorFlow, PyTorch, or Scikit-learn.
Strategic Collaborator: You excel at working across functional lines - Translating Data Science needs into engineering requirements and mentoring others on best practices.

Nice To Haves

Big Data Enthusiast: You ideally have experience with Snowflake, Databricks, or streaming technologies like Kafka to handle event-base data at scale.

Responsibilities

Architect the ML Ecosystem: You will own the end-to-end lifecycle of our ML infrastructure, designing a scalable, modern environment that enables models to thrive in production.
Productionize Innovation: Partner closely with our Data Science team to take complex algorithms from the "lab" to the "real-world", building the high-performance pipelines required to scale them.
Engineer Feature Intelligence: Design and maintain both offline and online feature stores, ensuring our models have the high-quality data they need for instant decision-making.
Escale the Data Platform: Collaborate with Data Engineers to evolve our data lake and ETL architectures, ensuring our data platform remains robust and future-proof.
Ensure System Health: Lead the monitoring and observability strategy for all production models, ensuring reliability and performance through proactive maintenance.
Shape Technical Strategy: Act as a key stakeholder in architectural decisions, helping the broader team define the strategy for our data products and event-driven architectures.