Machine Learning Engineer - Post Training

Mindbeam; United States; On-site; USD 100,000 - 150,000; Full time

Job summary

Mindbeam is seeking a skilled professional to develop next-generation AI infrastructure. The role focuses on building pipelines for post-training tasks, implementing scalable deployment systems, and collaborating with researchers to ensure effective model validation.

Candidates should have strong Python skills, experience in model deployment, and a background in computer science or a related field. This role combines research and practical application, driving advancements in AI.

Qualifications

• Minimum 2 years of experience in model training, evaluation, or deployment.
• Hands-on experience deploying models on cloud and GPU infrastructure.
• Strong skills in Python and familiarity with ML frameworks.

Responsibilities

• Develop pipelines for post-training tasks such as fine-tuning and evaluation.
• Implement scalable systems for model deployment and optimization.
• Collaborate with researchers to validate experimental results.

Skills

Python

ML frameworks (PyTorch/TensorFlow)

Data pipeline tools

Optimization techniques

Monitoring and observability tools

Education

Bachelor’s, Master’s, or PhD in Computer Science, ML/AI, or related field

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.