AI Research Engineer

Bright Vision Technologies•Monroe Township, MI

1d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an AI Research Engineer to bridge applied AI research and production engineering. The role involves designing and shipping advanced machine learning systems for high-impact business problems, combining scientific rigor with practical software engineering. The engineer will work across the full lifecycle, from problem framing and experimentation through deployment and continuous improvement, and will assess evolving AI research for real-world applicability. The position is 100% remote in the United States and is full-time, direct W2. The stated annual salary range is $100,000–$150,000. Applicants with 12 or more years of combined research and applied ML engineering experience are sought. Bright Vision Technologies is unable to sponsor new H-1B visa petitions, though U.S. citizens, Green Card holders, EAD holders, and candidates seeking an H-1B transfer are encouraged to apply.

Requirements

Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field, or equivalent applied experience.
12 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 the 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 research in deep learning, large language models, and related areas, and assess its applicability to internal use cases.
Implement rigorous experimentation workflows, including baselines, ablations, and statistically sound evaluation methodologies.
Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools.
Collaborate with ML platform engineers to use compute, storage, and accelerator resources efficiently.
Optimize models for accuracy, latency, throughput, and cost according to production requirements.
Develop tooling for dataset construction, labeling, validation, and ongoing data-quality monitoring.
Partner with product, design, and domain experts to align model behavior 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 for 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.