Job Overview
Location
United States
Job Type
Full-time
Category
Software Engineering
Date Posted
May 22, 2026
Full Job Description
đź“‹ Description
- • Develop end-to-end pipelines for post-training tasks including fine-tuning, model evaluation, and compression to enhance AI performance and efficiency.
- • Design and implement scalable systems for deploying machine learning models in production environments with focus on reliability and performance.
- • Collaborate directly with research teams to validate experimental AI models in real-world production contexts and ensure reproducibility of results.
- • Build automated tools for benchmarking model performance and conducting regression testing across model versions and configurations.
- • Optimize resource utilization and inference speed by identifying bottlenecks and applying efficiency improvements such as quantization, pruning, and knowledge distillation.
- • Engineer monitoring and observability solutions to track model behavior, drift, latency, and resource consumption in live deployments.
- • Work with cloud and GPU-based infrastructure to deploy, scale, and maintain machine learning models across distributed environments.
- • Translate research prototypes into production-grade systems that meet enterprise-grade standards for latency, throughput, and fault tolerance.
- • Integrate model evaluation metrics into continuous integration pipelines to ensure consistent performance across updates.
- • Contribute to the development of internal tools that streamline the post-training workflow for large-scale AI applications.
- • Maintain documentation and best practices for model deployment pipelines to support cross-team adoption and knowledge sharing.
- • Proactively identify opportunities to reduce computational costs and improve energy efficiency in model inference workflows.
- • Participate in code reviews and technical design discussions to uphold high standards of software engineering and ML system architecture.
- • Stay current with advancements in ML infrastructure, optimization techniques, and deployment frameworks to inform system improvements.
🎯 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
🏖️ Benefits
- • Competitive salary and equity compensation
- • Comprehensive health, dental, and vision insurance
- • Unlimited paid time off
- • Remote-first work environment with flexible hours
- • Professional development stipend for conferences and courses
- • Latest hardware and software tools provided
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Mindbeam AI
Mindbeam AI is a New York City–based startup specializing in next-generation AI infrastructure. Its flagship product, Litespark, is a framework designed to accelerate the pre-training and fine-tuning of large language models (LLMs). Litespark utilizes advanced algorithms to significantly reduce training times—from months to days—while minimizing costs and energy consumption. The framework is compatible with industry-standard machine learning frameworks like PyTorch, TensorFlow, and JAX, and is optimized for NVIDIA GPU hardware. Mindbeam's solutions are utilized by Fortune 100 enterprises and are available on AWS Marketplace.
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