Machine Learning Engineer - Post Training

Role Overview

We are building the next-generation AI infrastructure for both open-source and enterprise applications. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level. Advance AI performance and efficiency by engineering systems for fine-tuning, evaluation, and deployment at scale.

What You Will Do

Develop pipelines for post-training tasks such as fine-tuning, evaluation, and model compression. Implement scalable systems for model deployment, monitoring, and optimization. Collaborate with researchers to validate experimental results in production contexts. Build tools to automate benchmarking and regression testing. Identify opportunities to improve efficiency in resource utilization and inference speed.

Why It Might Be a Fit

You combine deep technical expertise with a pragmatic mindset. You thrive on bridging research and production, and you’re motivated by the challenge of making cutting-edge models usable and efficient at scale.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, ML/AI, or related field—or equivalent practical experience.
• 2+ years of experience in model training, evaluation, or deployment.
• Strong skills in Python, ML frameworks (PyTorch/TensorFlow), and data pipeline tools.
• Familiarity with optimization techniques (quantization, pruning, distillation).
• Hands-on experience deploying models on cloud and/or GPU infrastructure.
• Knowledge of monitoring and observability tools.