AI Research Engineer

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. Job Title: AI Research Engineer Location: 100% Remote (Continental United States) Position Type: In‑house Bright Vision Technologies SOW engagement (no third‑party client or vendor) Salary: $100K - $150K Experience: 6+ years Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. Employment Type: Full‑time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third‑party) Engagement: Long‑term, multi‑year, aligned to the Bright Vision SOW delivery roadmap Compensation: Competitive base salary commensurate with experience, plus benefits. Employment Terms & Visa Policy: This is a 100% remote, full‑time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in‑house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third‑party client, vendor, or implementation partner involved. Candidates must be willing to work directly as a full‑time W2 employee of Bright Vision Technologies and contribute to our in‑house SOW deliverables. No new H1B sponsorship is available for this role, but existing H1B holders are welcome to apply and we will support H1B transfers for qualified candidates. 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 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. Required Qualifications 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 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. Preferred Qualifications 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. How to Apply For immediate consideration, please send your resume to View email address on click.appcast.io . Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment. #J-18808-Ljbffr