Mindbeam
Engineering
MachineLearningEngineer-PostTraining
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“Machine Learning Engineer - Post Training at Mindbeam. Skills: model training, model evaluation, model deployment, fine-tuning, model compression, optimization techniques, monitoring, observability. Develop pipelines for post-training tasks such as fine-tuning, evaluation, and model compression. Implement scalable systems for model deployment, monitoring, and optimization”
What You'll Achieve.
Advance AI performance and efficiency by engineering systems for fine-tuning, evaluation, and deployment at scale
Industry & Context.
Identify opportunities to improve efficiency in resource utilization and inference speed
What They're Looking For.
Must Have
2+ years of experience in model training, evaluation, or deployment, skills in Python, ML frameworks (PyTorch/TensorFlow), data pipeline tools, Hands-on experience deploying models on cloud and/or GPU infrastructure
Nice to Have
Familiarity with optimization techniques (quantization, pruning, distillation), Knowledge of monitoring and observability tools
What You'll Do.
Develop pipelines for post-training tasks such as fine-tuning
and model compression
Implement scalable systems for model deployment
Build tools to automate benchmarking and regression testing
Identify opportunities to improve efficiency in resource utilization and inference speed
How You'll Work.
Team & Collaboration
Collaborate with researchers to validate experimental results in production contexts
Full Job Description
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.
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