Bright Vision Technologies

This full-time LLM Fine-Tuning Engineer role is based within Bright Vision Technologies' in-house engineering team, operating 100% remotely across the continental United States. The position focuses on designing and executing fine-tuning workflows for large language models using techniques such as RLHF, DPO, and parameter-efficient methods like LoRA. Key responsibilities include building scalable distributed training pipelines, curating high-quality datasets, implementing rigorous evaluation suites, and optimizing GPU cluster performance for production environments. The role appeals to candidates seeking a direct W2 engagement with long-term stability, offering significant career growth within a forward-thinking organization. It provides the opportunity to mentor junior engineers, collaborate across cross-functional teams, and translate cutting-edge research into reliable, scalable solutions without third-party constraints.

This full-time AI Performance Optimization Engineer role is based within Bright Vision Technologies' in-house engineering team and operates 100% remotely across the United States. The position focuses on maximizing throughput, minimizing latency, and reducing costs for large-scale neural network training and inference systems. Key responsibilities include profiling and optimizing end-to-end AI pipelines, implementing model compression strategies like quantization and pruning, and driving compiler-level improvements using tools such as Triton and XLA. The role appeals to candidates seeking deep technical impact in a collaborative environment where they can mentor junior engineers and shape production best practices. The company offers a direct W2 employment structure with long-term project alignment and a culture that values rigorous data-driven engineering and continuous innovation in AI systems.

This full-time Edge AI Engineer role is based within Bright Vision Technologies' in-house engineering team, focusing on deploying machine learning models onto resource-constrained edge devices. The position involves designing optimized AI solutions for mobile and embedded hardware, applying techniques like quantization and pruning to enhance performance, and building secure cross-platform inference runtimes. Key responsibilities include tuning models for latency and energy efficiency, managing on-device update workflows, and collaborating with hardware teams to align AI capabilities with device constraints. The position offers a 100% remote work arrangement within the continental United States and provides significant career growth potential within a forward-thinking organization. Candidates will join a collaborative culture that values innovation, technical excellence, and the development of scalable, secure applications.