> Markdown version of [/jobs/ext/1388991-senior-software-engineer-mlops](https://www.wearedevelopers.com/jobs/ext/1388991-senior-software-engineer-mlops). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer, MLOps - **Company:** Forward, Inc. - **Location:** Ontario, CA, United States - **Experience:** Expert - **Salary:** $175,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Agile Methodology, Airflow, Data Analysis, Software Applications, Code Review, Databases, Information Engineering, Relational Databases, Software Design Patterns, Python (Programming Language), PostgreSQL, Machine Learning, Query Optimization, Service-Oriented Architecture, Software Engineering, Backend, Information Technology, Low Latency, Machine Learning Operations - **Published:** July 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0e0694092e874f51 ## About the Role * 5+ years of software experience, with a focus on backend systems (Python required) and 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 mutli-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, * 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 ## Description * 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 our 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 * Contributing to best practices and raising the bar through thoughtful code reviews and contributions to technical design discussions Why you should apply: * Mission-driven company: Forward is a trusted source of fast, flexible funding for small businesses that have often been underserved by traditional financing options. When you join the team, you will help ensure all small businesses have access to the financial support they need to succeed. * Flexibility is a top priority: Our employees are empowered to choose where they want to work (whether that's from home, in the office, or a combination of both)., * Keep It Real: We value direct communication, candid feedback, and authenticity. We are an open book. * Act With Kindness: We create an environment where caring is cool and helping is the norm. We do the right thing. * Shoot for Extraordinary: We are inspired by innovative thinking and continuous improvement. We never settle for yesterday's best. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)