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About This Role
Job Overview: We are seeking a highly skilled and innovative Staff Research Engineer specializing in Frontier AI, Reinforcement Learning (RL), and Evaluation. In this role, you will lead cutting-edge research efforts to develop large-scale AI models, contribute to advancing the state of the art in machine learning, and collaborate with cross-functional teams to translate research insights into impactful applications.
Our environments evaluate and improve AI models on complex, long-range, multi-step workflows across high-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, and Customer Experience.
Our work includes:
- Software engineering environments for coding agents
- UI environments for computer-use and browser-use agents
- MCP-based environments for function-calling agents across enterprise and consumer applications
We are looking for Staff Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training.
This role sits at the intersection of research and engineering. You will investigate high-impact questions, design rigorous experiments, build research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications.
You will contribute to areas including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
Rate: $40/hr
Employment Type: Short Term Contract 1099
Location: WFH in ONLY these states
Alabama
Arizona
Colorado
Florida
Idaho
Iowa
Kentucky
Michigan
Minnesota
Mississippi
Missouri
Montana
New York
North Carolina
North Dakota
Oklahoma
Pennsylvania
South Carolina
South Dakota
Virginia
Wisconsin
Wyoming
Shift: 40 hours a week with a 4hr overlap with Pacific Time business hours
Responsibilities:
Conduct Research on Frontier AI Systems
- Investigate the capabilities, limitations, and training methods of frontier AI systems.
Build and Evaluate Research Systems
- Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks.
- Train, test, and evaluate models using modern AI and machine learning tools.
- Analyze experimental results and draw clear, evidence-based conclusions.
- Establish strong practices for experimental rigor, data quality, reproducibility, and interpretation.
- Iterate quickly from initial hypotheses to validated technical insights.
Translate Research into Practical Impact
- Collaborate with Research, Engineering, Product, and Operations teams.
- Translate research findings into improvements for Turing’s products, platforms, and AI capabilities.
- Identify which ideas are ready to move from exploration into scalable, real-world applications.
- Communicate technical findings clearly to both specialized and cross-functional audiences.
Contribute to the Research Community
- Share findings through technical reports, publications, open-source work, workshops, or conferences where appropriate.
- Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration.
- Represent Turing thoughtfully within the broader AI research community.
- Formulate research questions that inform Turing’s products, platforms, and technical strategy.
- Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
- Stay current with advances in machine learning and identify opportunities for meaningful technical contributions.
Qualifications:
- PhD or Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, or a closely related technical field. Exceptional equivalent research experience will also be considered.
- Minimum 7+ years of professional experience with significant research engineering experience working on machine learning or frontier AI systems.
- Strong foundations in machine learning and practical experience designing experiments, training models, evaluating models, or developing AI systems.
- Demonstrated research experience in one or more of the following areas:
- Synthetic or agentic data generation
- Reinforcement learning or post-training
- Model understanding
- AI evaluation
- Benchmarks
- AI agents or tool-using systems
- Strong programming skills, particularly in Python, with the ability to implement, test, and iterate quickly in a research environment.
- Experience with modern AI and machine learning frameworks and tooling.
- Strong scientific judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making.
- Excellent written and verbal communication skills.
- Ability to work independently and collaborate effectively across research and engineering teams.
- Experience mentoring engineers or researchers and providing technical leadership.
Join us to be at the forefront of artificial intelligence innovation, contributing to transformative advancements that shape the future of technology.
Pay: $40.00 per hour
Expected hours: 40.0 per week
Work Location: Remote
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 9,079 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Fusion BPO Services, 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 $252,000 based on 457 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and AI Engineering Manager ($251,622). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Fusion BPO Services AI Hiring
Fusion BPO Services has 1 open AI role right now. They're hiring across Research Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
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