Generative AI Engineer - Remote Opportunity
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent experience.
- Six or more years of combined machine learning research and engineering experience, including significant exposure to large language models.
- Strong proficiency in Python and modern deep learning frameworks, particularly PyTorch.
- Hands-on experience fine-tuning transformer-based language models at meaningful scale.
- Familiarity with distributed training techniques, including FSDP, ZeRO, and pipeline parallelism.
- Experience with RLHF, Direct Preference Optimization (DPO), or other preference optimization methods.
- Strong understanding of model evaluation methodologies, benchmarks, and human evaluation design.
- Experience running training jobs on GPU clusters and recovering from failures.
- Strong written and verbal communication skills.
- A track record of delivering or publishing impactful LLM work.
Nice to have
- Publications at leading machine learning conferences or research venues.
- Experience fine-tuning multimodal models.
- Familiarity with synthetic data generation and dataset distillation.
- Open-source contributions to LLM training libraries.
- Exposure to responsible AI evaluation and red-teaming practices.
Full description
About the Company
Bright Vision Technologies is a technology consulting and software development company that delivers cloud, AI, data, and enterprise solutions across the United States.
About the Role
Bright Vision Technologies is seeking a Generative AI Engineer to design, develop, and operationalize fine-tuning workflows for large language models (LLMs). The role covers supervised fine-tuning, preference-based optimization, and reinforcement learning approaches, with a focus on dataset quality, model evaluation, distributed training, and reliable production pipelines.
You will collaborate with product, design, engineering, operations, and business stakeholders to translate complex requirements into practical technical solutions. The role also involves contributing to code and design reviews and mentoring junior engineers.
Key Responsibilities
- Design, implement, and operationalize fine-tuning workflows for large language models across supervised and preference-based learning approaches.
- Build and refine datasets to support effective model training and reliable evaluation.
- Apply appropriate evaluation methods, benchmarks, and human evaluation practices to measure model performance.
- Work with distributed training strategies and GPU clusters to run large-scale model training.
- Monitor training jobs, troubleshoot failures, and implement recovery procedures to maintain reliable workflows.
- Balance data quality, compute costs, evaluation requirements, and delivery timelines when making engineering decisions.
- Collaborate with cross-functional teams to translate business and product requirements into well-engineered solutions.
- Participate in code reviews and design reviews to maintain engineering quality and consistency.
- Mentor junior engineers and contribute to technical standards and team development.
- Deliver production-ready LLM solutions and contribute to impactful research or engineering outcomes.
Required Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent experience.
- Six or more years of combined machine learning research and engineering experience, including significant exposure to large language models.
- Strong proficiency in Python and modern deep learning frameworks, particularly PyTorch.
- Hands-on experience fine-tuning transformer-based language models at meaningful scale.
- Familiarity with distributed training techniques, including FSDP, ZeRO, and pipeline parallelism.