ML Engineer, Forward Deployed
Prior Labs4 months ago
Berlin, Germany or New York, NY, USASenior
Responsibilities
- Integrate tabular foundation models into customer platforms, cloud environments, and ML pipelines.
- Frame real-world data problems, engineer features, build and benchmark models, and compare results with customer baselines.
- Own customer use cases from initial conversation through reliable, documented production delivery.
- Partner with customer data science and ML teams as a trusted technical peer.
- Customize and optimize models across performance, latency, scale, and cost tradeoffs.
- Translate deployment insights into prioritized feedback for the model and product roadmap.
- Establish deployment patterns and standards for the growing team.
Requirements
- At least 3 years of experience building and deploying ML systems in production, with end-to-end ownership of challenging problems.
- Strong engineering fundamentals and expert-level Python skills.
- Hands-on ML expertise with PyTorch, scikit-learn, and transformer or foundation-model approaches.
- Depth in tabular, time-series, or structured-data machine learning.
- Production cloud deployment experience with AWS, GCP, or Azure.
- Ability to navigate technical and business stakeholders and drive customer outcomes.
- High autonomy, sound judgment in ambiguity, and a focus on clean, maintainable, well-documented code.
- Preferred: forward-deployed, solutions engineering, or senior technical customer-facing experience.
- Preferred: contributions to relevant open-source ML or data engineering projects.
- Preferred: experience with enterprise data ecosystems, APIs, and deployment pipelines.
Benefits
- Teams are based in Berlin, Freiburg, and New York, with exceptional remote arrangements generally involving frequent travel to an office.
- The company holds regular all-company offsites.
- The company offers equal opportunities and welcomes applicants from diverse backgrounds.
Tech Stack
Categories
Forward Deployed
About Prior Labs
Prior Labs builds foundation models that understand tabular data in spreadsheets and databases for researchers and enterprises across science, healthcare, finance, and operations. The privately held company, founded in 2024 and based in Freiburg and Berlin, commercializes its models and tooling; its TabPFN v2 was a Nature cover story and is deployed with Oxford Cancer Analytics for lung disease detection and with Hitachi to help prevent train failures.