Senior ML Ops Engineer | Hybrid + Equity | AI Powered Outage Intelligence SaaS Startup

PhillyTech.Co•King of Prussia, PA

2d•$120,000 - $130,000•Hybrid

About The Position

This is a 3-day in-office hybrid role in King of Prussia, PA. The company is a fast-growing B2B SaaS outage intelligence company helping major enterprises reduce downtime through advanced automation and real-time intelligence. Their technology helps organizations understand outages faster, automate operational workflows, reduce unnecessary costs, and accelerate repair times. Their platform supports critical infrastructure operations where reliability matters. As the company expands its customer base and develops new product, they are investing further in the machine learning systems behind their intelligence platform. This role requires building an ML stack from the ground up, understanding production ML infrastructure at scale, and having meaningful ownership over the systems supporting machine learning in production. It's an opportunity to join a growing technology company with significant ownership of the ML platform, working directly with engineers building the models that power the product. You will own and influence the ML platform from model training through production deployment and monitoring, build systems that customers rely on in real time, work directly with ML engineers on model development, evaluation, and productionization, help shape technical architecture and the future of the company's ML and operational infrastructure, and solve complex engineering problems involving machine learning, large-scale data pipelines, external data sources, and real-time systems. This is an entrepreneurial environment where engineers are expected to take ownership and influence how things are built, with an opportunity to earn equity in a growing technology company.

Requirements

4+ years of professional software engineering or data engineering experience.
2+ years building and operating machine learning systems in production.
Hands-on experience building an ML platform from the ground up or significantly scaling an existing ML platform.
Experience continuing to own and operate ML infrastructure as it matured.
Experience supporting multiple production models with complex training workloads.
Experience building and maintaining production data pipelines at scale, including managing their operating costs.
Experience working with multiple external data sources that behave differently and may arrive inconsistently.
Working knowledge of machine learning modeling and evaluation, with enough depth to review and challenge the work of ML engineers.
Entrepreneurial mindset and interest in working within a fast-moving startup environment.

Nice To Haves

Advanced proficiency with Python 3 in mature production environments + Kubernetes + PostgreSQL.
Experiment tracking, model registry, and model serving frameworks.
Workflow orchestration.
Infrastructure as code tooling.
Geospatial or time series systems.
Large real-time data flows.
Experience working for a data intelligence company.
Bachelor's degree in Computer Science or a related field.

Responsibilities

Own and extend the ML platform end to end, including training orchestration, experiment tracking, model registry, deployment, and production monitoring.
Build and operate data pipelines supporting model training and online inference.
Design processes for backfills, replays, and recovery when upstream data feeds fail.
Ensure training data accurately reflects what was known at the point in time it represents.
Build and manage reliable processes for moving trained models into production.
Ensure model training runs and results are reproducible.
Monitor deployed model performance over time, including models where outcomes are confirmed later.
Manage training and inference costs as data volume and the number of production models grow.
Contribute to technical architecture design and reviews.