AI Research Engineer

Bright Vision Technologies•North Brunswick, NJ

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 by designing and shipping advanced machine learning systems that address high-impact business problems. The role combines scientific rigor with practical software engineering and spans the full lifecycle of AI work—from problem framing and experimentation through deployment and continuous improvement. The engineer will work across natural language, computer vision, recommendation, and structured data domains; evaluate emerging research for real-world applicability; and build robust, scalable, well-instrumented production pipelines. The role also includes collaboration with product, design, domain, and ML platform teams, as well as mentoring engineers and influencing the organization’s AI roadmap. This is a full-time, direct W-2 position that is 100% remote within the U.S., with an annual salary range of $100,000–$150,000.

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 adjacent 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 ensure efficient use of compute, storage, and accelerator resources.
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 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 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.