AI Research Engineer

Bright Vision Technologies•South Plainfield, NJ

1d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The company is seeking an AI Research Engineer to bridge applied research and production engineering by designing and shipping advanced machine learning systems for high-impact business problems. The role combines scientific rigor with practical software engineering and spans the full lifecycle, from problem framing and experimentation through deployment and continuous improvement. The engineer will work across natural language, vision, recommendation, and structured data applications; evaluate emerging research for real-world use; and build robust, scalable, well-instrumented machine learning pipelines. The role also includes collaboration with product and engineering partners, responsible AI evaluation, clear documentation, mentoring, and contributions to the organization’s AI roadmap and internal knowledge sharing. This is a 100% remote, full-time, direct W2 position in the United States. The listed annual salary range is $100,000–$150,000. The employer cannot sponsor new H-1B visa petitions, but encourages U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates 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 machine learning.
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 machine learning formulations with clear success metrics and evaluation strategies.
Stay current with 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 machine learning 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 to meet 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.