Machine Learning Research Engineer

Bright Vision Technologies; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a Machine Learning Research Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems for high-impact business problems.

The role blends scientific rigor with software engineering, requiring deep understanding of ML, DL techniques, and building robust, scalable, production pipelines. 100% remote in the continental US, W2 employment, SOW engagement.

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.
• Stay current with deep learning and large language models and assess applicability to use cases.
• Implement rigorous experimentation workflows including baselines, ablations, and statistical evaluation.

Job description

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 Machine Learning Research Engineer 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.

Job Title: Machine Learning 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 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.

We do not engage in C2C, 1099, or third-party arrangements for this role.

BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE.

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.

However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates.

For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience.

Job Summary

We are seeking an Machine Learning 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,