Machine Learning Research Engineer

Bright Vision Technologies•Sugar Land, TX

1d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking a Machine Learning Research Engineer to connect applied research with production engineering. The role involves designing and shipping advanced machine learning systems for high-impact business problems, combining scientific rigor and practical software engineering. The engineer will work across the full lifecycle—from problem framing and experimentation to deployment and continued improvement—while evaluating new AI research for real-world applicability and building robust, scalable, well-instrumented production pipelines. This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies, supporting the company’s in-house Statement of Work engagement. Bright Vision Technologies is the employer and there is no third-party client, vendor, or implementation partner. C2C, 1099, and third-party arrangements are not accepted. The role offers a salary of $100,000–$150,000 and requires at least six years of experience. No new H1B sponsorship is available, but H1B transfers are welcomed for qualified candidates. A technical coding assessment is mandatory.

Requirements

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 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.
Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies; C2C, 1099, and third-party arrangements are not accepted.
A technical coding assessment is mandatory.
No new H1B sponsorship is available; qualified candidates who need an H1B transfer are welcome to apply.

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.
Keep 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 methodology.
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 using research insights, capability gaps, and emerging opportunities.