Senior Software Engineer, MLOps

Forward Financing•Boston, MA

1d•$175,000 - $220,000•Hybrid

About The Position

Forward Financing is seeking an experienced Senior Software Engineer to join their Engineering team. This role is responsible for the systems that power the company's online (real-time) feature store, which is the infrastructure that serves ML features to production models with low latency and high reliability. The Engineering team is a key driver of innovation, building the software that powers a FinTech product serving small businesses. A significant part of the company's strategy involves leveraging data from numerous sources to support AI/ML initiatives and analytical capabilities.

Requirements

5+ years of software experience, with a focus on backend systems (Python required)
2-3 years of MLOps experience
Strong experience with relational databases (Postgres preferred) — schema design, query optimization, and operating databases under production load
Experience building and operating real time inference systems
Understanding of the unique reliability and correctness demands of ML-serving infrastructure (data freshness, training/serving skew, feature consistency)
Experience in multi-service architectures and design patterns
Experience in Agile software development
Typically has a Bachelor's degree in Computer Science, Data Engineering, or a related field, or additional relevant experience

Nice To Haves

Experience with feature stores (e.g., Feast, Tecton, SageMaker Feature Store) or building an equivalent system in-house
Experience with MLOps tooling (e.g., MLflow, Airflow, dbt) and ML model deployment/serving patterns
Experience designing and implementing complex systems across multiple software applications and/or languages
Excellent written and verbal communication
Ability to influence others
Demonstrated project management skills

Responsibilities

Design, build, and operate the online feature store — the real-time serving layer that delivers ML features to production models with low latency and strong consistency guarantees
Build and maintain data pipelines (batch and streaming) that compute, validate, and publish features from source systems into the online and offline stores
Work on the data models and Postgres schemas that back real-time feature serving, optimizing for query performance, freshness, and scale
Act as a technical leader for feature store infrastructure; help drive enhancements to quality, scalability, reliability, and observability for the systems ML models depend on
Partner closely with Data Science, Analytics Engineering, Product Management, and Application Development teams to translate business requirements into production-grade engineering solutions
Contribute to mentoring for junior engineers
Contribute to best practices and raising the bar through thoughtful code reviews and contributions to technical design discussions