ML Engineer, Forward Deployed

Core Areas of Impact

A model that tops a benchmark and a model that changes how an organization works are two different things. You'll take our tabular foundation models into our most 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: You'll own engagements end-to-end with high autonomy, make hard technical calls under ambiguity, and work shoulder-to-shoulder with customer data science teams as a senior peer. The patterns you uncover in the field won't stay in the field - they flow straight back to our researchers and shape what we build next. You won't just deploy models; you'll help shape how Prior Labs deploys as we scale.

• Integrate: Embed our foundation models into customer platforms, cloud environments, and ML pipelines.

• Do the Data Science: Frame the problem on real, messy data, engineer features, model it, and benchmark rigorously against the customer's current baseline.

• Own Delivery: Carry use cases end-to-end - from first conversation to a reliable, documented production solution you stand behind.

• Partner Deeply: Work with customer data science and ML teams as a peer, earn their trust, and make them faster with our models.

• Tailor & Optimize: Customize models for diverse use cases, trading off performance, latency, scale, and cost.

• Close the Loop: Turn deployment insights into sharp, prioritized feedback that shapes the model and product roadmap.

• Set the Standard: Establish the deployment patterns the team builds on as we scale.

What We’re Looking For

• 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.

• High autonomy and sound judgment in ambiguity, and a bias toward clean, maintainable, well-documented code.