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

• Full-time
• Compensation: USD 165,000 - USD 175,000 - yearly

This is a 3-day in-office hybrid role in King of Prussia, PA

Are you ready to build and scale the machine learning infrastructure powering real time outage intelligence for some of the world’s most critical infrastructure?

Our client 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 products, they are investing further in the machine learning systems behind their intelligence platform.

This is not a role where you inherit a finished ML platform and simply maintain it. They are looking for someone who has previously helped build an ML stack from the ground up
, understands what production ML infrastructure looks like as it scales, and wants meaningful ownership over the systems supporting machine learning in production.

This is an opportunity to join a growing technology company where you will have significant ownership of the ML platform and work directly with the engineers building the models that power the product.

• 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.
• Solve complex engineering problems involving machine learning, large scale data pipelines, external data sources, and real time systems.
• Join an entrepreneurial environment where engineers are expected to take ownership and influence how things are built.
• Opportunity to earn equity in a growing technology company.
• Hybrid work model, onsite in King of Prussia 3 days per week
• Equity in a fast-scaling SaaS company
• Fully paid medical, dental, and vision options
• Life and AD&D insurance
• Unimited PTO

As a Senior MLOps Engineer, you will have a high level of ownership over the platform the machine learning team builds on, spanning model training, experimentation, deployment, monitoring, and the data infrastructure supporting production ML.

You must have firsthand experience building an ML platform from the ground up or taking an existing platform through significant scaling and continuing to operate it as it matured.

• 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.

• 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.

• Advanced proficiency with Python 3 in mature production…