PhillyTech.Co seeks a Senior Machine Learning Engineer & Scientist to join our core, senior ML team. In this high-impact, hands-on role, you will build and scale real-time ML models that directly shape our product and drive intelligent decision-making for enterprise clients.

What You'll Do

Build, update, improve, and maintain a suite of ML models used by enterprise clients.
Research, design, and prototype ML models that drive insights across customer data.
Develop new strategies to use ML to further business goals.
Collaborate with product and backend teams to ship ML-driven features directly into the SaaS platform.
Design and develop ML models to extract, classify, and rank metadata signals from large-scale datasets.
Own the full ML lifecycle: from prototyping → training → evaluation → deployment → monitoring.
Collaborate closely with the existing ML engineer to increase development velocity.
Explore and implement new ways to apply ML science to real-world business goals.
Stay current on ML trends and evaluate how to apply them for strategic advantage.
Contribute meaningfully to the team’s technical vision and infrastructure.

What We're Looking For

5-8+ years of machine learning engineering experience in B2B SaaS companies.
Hands-on experience with data pipelines, MLOps, and cloud platforms (AWS, GCP, or Azure).
Proven experience deploying ML systems at scale and a strong applied background in machine learning.
Demonstrated ML thought leadership in your current or previous roles.
A solid foundation in applied mathematics: numerical methods, optimization, linear algebra, controls and systems theory, and a deep understanding of classical statistical learning theory.
The ability to translate theory into working results with minimal oversight.
Philadelphia area-based and available for 3 days/week onsite in King of Prussia.
A strong ability to deliver, solve, and iterate in a fast-paced startup environment.
Proficiency in SQL and at least one additional programming language.
Low ego, high collaboration – you build with the team and for the problem.
Demonstrated ability to learn new technical material independently.

Nice to Have

Backgrounds in data intelligence, finance, insurance, security, or anti-fraud.
Experience with geospatial data and deep learning frameworks for multimodal modeling.
Preferred language: Python (but we’re open to exceptional talent regardless of stack).