ML Engineer, Forward Deployed

You'll take Prior Labs' tabular foundation models into strategic customers' environments — integrating them into real platforms and pipelines, doing the hands-on data science to prove value, and owning the implementation through to production. This is a senior role with high autonomy: you'll own engagements end-to-end, make hard technical calls under ambiguity, and work shoulder-to-shoulder with customer data science teams as a senior peer.

What you'll do

• Embed our foundation models into customer platforms, cloud environments, and ML pipelines.
• Frame the problem on real, messy data, engineer features, model it, and benchmark rigorously against the customer's current baseline.
• Carry use cases end-to-end — from first conversation to a reliable, documented production solution you stand behind.
• Work with customer data science and ML teams as a peer, earn their trust, and make them faster with our models.
• Customize models for diverse use cases, trading off performance, latency, scale, and cost.
• Turn deployment insights into sharp, prioritized feedback that shapes the model and product roadmap.

What you'll need

• 3+ years building and deploying ML systems in production, with a track record of owning hard problems end-to-end.
• Strong engineering fundamentals and expert-level Python.
• Deep, hands-on ML ability — you build models you understand and can defend, not just call an API. Strong with PyTorch and scikit-learn, with a solid grasp of transformer / foundation-model approaches.
• Depth in tabular, time series, or structured-data ML.
• Proven cloud deployment (AWS, GCP, or Azure) into production, kept reliable under real-world conditions.
• Mature customer instinct — you diagnose the real problem, navigate technical and business stakeholders, and drive to outcomes.

Nice to have

• Prior forward-deployed, solutions engineering, or senior technical customer-facing experience.
• Contributions to relevant open-source projects in ML or data engineering.
• Experience integrating with enterprise data ecosystems and designing APIs and deployment pipelines.