Research Engineer, Foundation Model

About the role

At Prior Labs, Research Engineers are the core of the science team. You will design experiments, contribute to academic papers, and write the code that turns architectural ideas into trained models. The role offers significant technical ownership and opportunities to grow as the team scales.

Key responsibilities

• Scale transformer architectures from 10 K to over 1 M samples without language‑model assumptions.
• Build multimodal models that combine tabular, text, and numerical data.
• Make models efficient enough for real‑world deployment, not just academic benchmarks.
• Design architectures for time‑series forecasting, anomaly detection, and related tables.
• Run ablations, analyze scaling behavior, and develop training and evaluation infrastructure for rapid experimentation.

Required profile

• Master’s or PhD in Computer Science or a related field with 3+ years of experience building ML systems.
• Publications at top ML venues (NeurIPS, ICML, ICLR) or equivalent impact through open‑source or deployed systems.
• Deep proficiency in Python, PyTorch, and the broader ML ecosystem (scikit‑learn, pandas, NumPy).
• Experience implementing and training neural network architectures, preferably transformers or foundation models.
• Solid understanding of training dynamics, scaling behavior, and common failure modes in deep learning.
• Strong interest in model efficiency and practical deployment.

Required skills

• Python
• PyTorch
• scikit‑learn
• pandas
• NumPy