AI Applications Engineer

Bright Vision Technologies; United States; Remote; USD 100,000 - 175,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Applications Engineer to design, implement, and optimize fine-tuning workflows for large language models. This remote role requires production-grade engineering practices and strong ML intuition to translate ambiguous requirements into robust, scalable solutions.

You will collaborate with product, design, and platform teams to ship end-to-end fine-tuning pipelines, evaluate models rigorously, and mentor engineers while staying current with the latest

Qualifications

• Master’s or PhD in Computer Science, ML, or related field or equivalent experience.
• Six or more years of ML research and engineering with significant LLM exposure.
• Strong Python and PyTorch proficiency with practical model fine-tuning experience.
• Familiarity with distributed training strategies and RLHF/DPO techniques.
• Experience operating training jobs on GPU clusters and robust eval design.

Responsibilities

• Design and execute fine-tuning experiments for LLMs using supervised, DPO, RLHF.
• Lead dataset construction, curation, and QA for instruction tuning and preferences.
• Build scalable training pipelines on modern distributed frameworks.
• Tune hyperparameters and training stability for large-model fine-tuning.
• Implement parameter-efficient fine-tuning such as LoRA/QLoRA and adapters.
• Design rigorous evaluation suites including automated benchmarks and human eval.
• Implement safety and policy evaluations across releases.
• Operate large-scale GPU training jobs, diagnose failures, recover state.
• Optimize throughput with mixed precision and efficient attention.
• Manage model artifacts and reproducibility across experiments.
• Collaborate with product/research/platform teams to align roadmaps.
• Document methodology, results, and decisions for technical audiences.
• Mentor engineers on best practices and responsible deployment.

Skills

Communication skills

Shipping LLM work

Python proficiency

PyTorch proficiency

Education

Master’s or PhD in CS/ML

Tools

PyTorch

FSDP

ZeRO

RLHF

DPO

LoRA

QLoRA

Adapter-based methods

Job description

AI Applications Engineer – Remote

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title: AI Applications Engineer

Location: 100% Remote (United States)

Position Type: Full-time, Direct W2

Salary Range: $100,000–$175,000 Annually

Experience: 6+ years

Sponsorship: U.S. Citizens, Green CardHolders, EADHolders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary:

We are looking for an AI Applications Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production.

Key Responsibilities:

• Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques.
• Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data.
• Build scalable training pipelines on top of modern distributed training frameworks.
• Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning.
• Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods.
• Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes.
• Implement safety, refusal, and policy evaluations to track model behavior across releases.
• Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably.
• Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations.
• Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments.
• Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs.
• Document training methodology, results, and decisions clearly for technical and non-technical audiences.
• Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment.
• Stay current with LLM research and translate advances into production-ready fine-tuning recipes.

Required Qualifications:

• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience.
• Six or more years of combined ML research and engineering experience, with significant LLM exposure.
• Strong proficiency in Python and modern deep learning frameworks, especially PyTorch.
• Hands-on experience fine-tuning transformer-based language models at non-trivial scale.
• Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism.
• Experience with RLHF, DPO, or other preference optimization techniques.
• Strong understanding of evaluation methodology, benchmarks, and human evaluation design.
• Experience operating training jobs on GPU clusters and recovering from failures.
• Strong written and verbal communication skills.
• Track record of shipping or publishing impactful LLM work.

Preferred Qualifications:

• Publications at top-tier ML venues.
• Experience with multimodal model fine-tuning.
• Familiarity with synthetic data generation and dataset distillation.
• Open-source contributions to LLM training libraries.
• Exposure to responsible AI evaluation and red-teaming practices.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) 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.