AI Research Engineer (Applied AI)

Bright Vision Technologies•Alpharetta, GA

1d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled AI Research Engineer (Applied AI) to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. 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.

Requirements

Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience.
Six or more years of combined research and applied ML engineering experience.
Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX.
Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale.
Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML.
Experience taking ML models from research prototype to production with appropriate observability and safeguards.
Familiarity with distributed training, mixed-precision training, and accelerator hardware.
Strong written and verbal communication skills, including ability to explain complex methods clearly.
Demonstrated ability to read, evaluate, and adapt techniques from current research literature.
Track record of shipping impactful applied AI projects.

Nice To Haves

Published research at top-tier AI/ML venues.
Experience with large language model training, fine-tuning, or evaluation.
Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures.
Exposure to responsible AI, model evaluation, and alignment practices.
Experience contributing to open-source ML projects.

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 training and inference pipelines using modern ML frameworks and orchestration tools.
Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources.
Optimize models for accuracy, latency, throughput, and cost based on production requirements.
Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality.
Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements.
Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions.
Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences.
Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment.
Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks.
Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities.