Senior ML Engineer: Tabular Foundation Models in Production

FDE Times; Town of Tonawanda (NY); Hybrid; USD 180.000 - 260.000; Vollzeit

Zusammenfassung

Prior Labs is seeking a senior ML engineer to own engagements end-to-end, embedding foundation models into customer platforms and ML pipelines. You’ll work with client data science teams, frame real-world data problems, engineer features, and benchmark models against baselines in production settings.

This role emphasizes autonomy, strong Python/PyTorch skills, and scalable deployment practices. You will collaborate across teams, adapt models to diverse use cases, and contribute to the ongoing

Qualifikationen

• 3+ years building and deploying ML systems in production with end-to-end ownership.
• Strong engineering fundamentals and expert-level Python.
• Deep, hands-on ML ability; proficient with PyTorch and scikit-learn; knowledge of transformer/foundation-model approaches.
• Depth in tabular, time series, or structured-data ML.
• Proven cloud deployment (AWS, GCP, or Azure) into production under real-world conditions.
• Mature customer instinct; able to diagnose problems and drive outcomes.

Aufgaben

• Integrate tabular foundation models into client platforms and pipelines.
• Frame real-world data problems, engineer features, model, and benchmark against baselines.
• Own use cases end-to-end from first conversation to production solution.
• Collaborate with client data science teams to accelerate adoption.
• Tailor models for diverse use cases balancing performance, latency, and cost.
• Turn deployment insights into actionable feedback for product roadmap.
• Set deployment patterns and contribute to scalable practices.

Kenntnisse

Python

PyTorch

scikit-learn

ML systems

Cloud deployment

Data engineering

Transformers

Tools

AWS

GCP

Azure

Jobbeschreibung

Prior Labs is seeking a senior ML engineer to own engagements end-to-end, embedding foundation models into customer platforms and ML pipelines. You’ll work with client data science teams, frame real-world data problems, engineer features, and benchmark models against baselines in production settings.

This role emphasizes autonomy, strong Python/PyTorch skills, and scalable deployment practices. You will collaborate across teams, adapt models to diverse use cases, and contribute to the ongoing