Machine Learning Engineer - Post Training
About Mindbeam
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.
Mission
Advance AI performance and efficiency by engineering systems for fine-tuning, evaluation, and deployment at scale.
Role Expectations
• 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.
Background
• 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.
About You
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.
Apply To This Job
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.
Mission
Advance AI performance and efficiency by engineering systems for fine-tuning, evaluation, and deployment at scale.
Role Expectations
• 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.
Background
• 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.
About You
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.
Apply To This Job