Machine Learning Research Engineer

Bright Vision Technologies•Highlands Ranch, CO

2d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking a Machine Learning Research Engineer to bridge applied research and production engineering by designing and shipping advanced machine learning systems for business problems. The role combines scientific rigor with practical software engineering, spanning problem framing, experimentation, deployment, and continuous improvement. The position is a full-time, direct W2 role with Bright Vision Technologies on an in-house Statement of Work engagement; no third-party client, vendor, or implementation partner is involved. The role is 100% remote within the Continental United States. Salary is $100,000–$150,000. No new H1B sponsorship is available, but qualified candidates needing an H1B transfer may apply. 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 machine learning 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 machine learning 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.
Candidates must be confident in their technical abilities and hands-on experience and complete a mandatory technical coding assessment.

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 in natural language, computer vision, recommendation, and structured data.
Translate ambiguous business problems into well-scoped machine learning formulations with clear success metrics and evaluation strategies.
Track current research in deep learning, large language models, and related areas, and assess its applicability to internal use cases.
Implement rigorous experimentation workflows, including baselines, ablations, and statistically sound evaluation methods.
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 according to production requirements.
Develop tools 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 use findings in 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.