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AI Research Engineer
This role is no longer accepting external applications
It closed on 9/29/2026. The full description is kept below for reference. You can express your interest directly via ResumeKart below to be placed in our priority candidate showcase for recruiters hiring for similar roles.
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Job Description
AI Research Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: AI Research Engineer Location: 100% Remote (U. S.) Position Type: Full-time, Direct W2 Salary Range: $100,000–$150,000 Annually Experience Required: 6+ years Sponsorship: U. S.
Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary
We are seeking an AI Research Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems.
The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines.
The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement.
Key Responsibilities
Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies.
Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. Build production-quality trai
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