LLM Fine-Tuning Engineer Jobs in USA 2026
Large language model development continues to shape the future of enterprise AI as organisations adopt advanced generative systems to automate workflows, improve customer experiences, and accelerate decision-making. This LLM Fine-Tuning Engineer opportunity offers experienced machine learning professionals the chance to design and operationalize advanced model training pipelines while contributing to production-grade AI solutions in a high-growth engineering environment. H-1B transfers are supported for eligible candidates.
About the Role
The LLM Fine-Tuning Engineer role focuses on designing, executing, and scaling fine-tuning workflows for large language models across supervised learning, preference optimization, and reinforcement learning approaches. The position involves ownership across the model lifecycle including dataset design, experimentation, training infrastructure, evaluation frameworks, deployment readiness, and operational reliability.
The role also includes collaboration across product, research, and engineering teams to translate evolving business requirements into production-ready AI capabilities.
About the Hiring Organisation
Bright Vision Technologies is a software development company focused on building scalable technology solutions that help organisations automate and optimize business operations. The company operates through an in-house Statement of Work delivery model and develops secure, cloud-enabled applications across enterprise environments. Teams work directly within long-term engagements focused on delivering measurable business outcomes through modern engineering practices.
Job Duties
- Design and execute fine-tuning experiments for large language models using supervised learning, DPO, RLHF, and related methodologies.
- Lead dataset construction, curation, validation, and quality assurance for instruction tuning and preference datasets.
- Build scalable model training pipelines using distributed training frameworks.
- Optimize hyperparameters, training configurations, and model stability strategies.
- Implement parameter-efficient approaches including LoRA, QLoRA, and adapter-based methods.
- Develop rigorous evaluation frameworks using benchmarks, human evaluation, and capability-specific testing.
- Implement safety evaluations, refusal testing, and behavioral monitoring across model releases.
- Operate large-scale GPU training workloads and manage failure recovery processes.
- Improve training efficiency using mixed precision, efficient attention methods, and throughput optimization.
- Maintain reproducibility, experiment tracking, and model lineage across training workflows.
- Collaborate with research, product, and platform teams to align model development priorities.
- Document methodologies, evaluation outcomes, and technical decisions for diverse stakeholders.
- Mentor engineers on fine-tuning methodology and responsible deployment practices.
- Track developments in LLM research and translate advancements into production workflows.
Job Requirements
- Master’s degree, PhD, or equivalent experience in Computer Science, Machine Learning, or a related discipline.
- Six or more years of machine learning research and engineering experience with meaningful LLM exposure.
- Strong proficiency in Python and deep learning frameworks, especially PyTorch.
- Hands-on experience fine-tuning transformer-based language models at scale.
- Experience with distributed training approaches including FSDP, ZeRO, and pipeline parallelism.
- Knowledge of RLHF, DPO, and preference optimization workflows.
- Strong understanding of evaluation methodologies and human assessment design.
- Experience operating GPU-based training infrastructure.
- Strong written and verbal communication skills.
- Demonstrated ability to deliver impactful LLM research or production outcomes.
- Experience with multimodal model fine-tuning is advantageous.
- Familiarity with synthetic data generation and dataset optimization techniques is beneficial.
- Exposure to responsible AI evaluation and red-teaming practices is valued.
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Conclusion
This LLM Fine-Tuning Engineer position provides an excellent opportunity for experienced AI professionals seeking deep ownership across modern language model development and deployment. With exposure to advanced training systems, large-scale experimentation, and long-term enterprise AI initiatives, the role offers meaningful career growth in production-grade machine learning engineering.
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