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About This Role
Machine Learning Research 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: Machine Learning Research Engineer
Location: 100% Remote (U.S.)
Position Type: Full\-time, Direct W2
Salary Range: $100,000–$150,000 Annually
Experience Required: 6\+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, 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 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 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
Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908\) 505\-3544\. Learn more about Bright Vision Technologies at www.bvteck.com.
Bright Vision Technologies is an Equal Opportunity Employer.
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.
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Salary Context
This $100K-$150K range is in the lower quartile for Research Engineer roles in our dataset (median: $188K across 44 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At BV Teck, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $280,000 based on 147 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($125K) sits 55% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BV Teck AI Hiring
BV Teck has 40 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, LLM Engineer, Research Engineer. Positions span Tempe, AZ, US, Hoboken, NJ, US, Remote, US. Compensation range: $150K - $175K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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