Interested in this Research Engineer role at Kadir?
Apply Now →Skills & Technologies
About This Role
FORWARD DEPLOYED RESEARCH ENGINEER
Location: Remote—United States Employment Type: Full-time Salary: $100,000–$200,000 per year Schedule: Approximately 70–80% working-hour overlap with San Francisco or Singapore Visa/Relocation Support: Available for exceptional candidates
ABOUT THE OPPORTUNITY
Kadir.ai is recruiting a Forward Deployed Research Engineer on behalf of a fast-growing artificial intelligence company developing reinforcement-learning training data, evaluations, and infrastructure for frontier AI agents.
This is a hands-on engineering position for someone who enjoys solving ambiguous, time-sensitive technical problems. You will work across AI evaluations, software, data pipelines, deployment environments, and customer delivery.
This is not a traditional implementation-consulting role. The successful candidate will combine strong technical judgment, practical debugging skills, and the ability to independently deliver solutions for technical customers and data partners.
KEY RESPONSIBILITIES
- Own technical deployment requests from initial triage through completion
- Clarify ambiguous customer requirements and identify the underlying technical problem
- Troubleshoot issues involving evaluation environments, datasets, configurations, code, and agent behavior
- Build scripts, tools, and specialized pipelines for urgent customer and partner requirements
- Clean, transform, validate, and analyze complex datasets
- Develop and improve benchmarks and evaluations for AI agents
- Evaluate task realism, rubric reliability, environment usability, and trajectory quality
- Debug problems across Python applications, Docker containers, Linux environments, and data pipelines
- Work directly with technical customers, subject-matter experts, and data vendors
- Coordinate with research, engineering, operations, and go-to-market teams
- Balance delivery speed with quality, correctness, security, and reliability
- Convert recurring manual processes into scalable tools and automated workflows
- Document technical findings, risks, procedures, and recommended solutions
- Communicate project status and technical issues clearly to stakeholders
REQUIRED QUALIFICATIONS
- Strong proficiency in Python
- Hands-on experience with Docker and Linux
- Experience with AI or machine-learning evaluations and benchmarks
- Experience developing data-processing, validation, or evaluation pipelines
- Strong debugging skills across software, data, and execution environments
- Understanding of evaluation quality, task design, scoring, rubrics, and failure analysis
- Ability to work independently in ambiguous and rapidly changing situations
- Strong technical judgment and first-principles problem-solving skills
- Excellent written and verbal communication abilities
- Experience collaborating with technical and non-technical stakeholders
- Ability to maintain approximately 70–80% working-hour overlap with San Francisco or Singapore
PREFERRED QUALIFICATIONS
- Experience in applied AI, research engineering, or forward-deployed engineering
- Experience building or maintaining reinforcement-learning environments
- Familiarity with AI agents, trajectories, graders, reward signals, and evaluation frameworks
- Experience supporting technical customers or urgent production deployments
- Experience working with frontier models or post-training workflows
- Experience cleaning and validating large or complex datasets
- Experience creating internal tools and automating recurring workflows
- Early-stage startup experience
- Published research, technical blogs, open-source contributions, or independent AI projects
- Experience collaborating with distributed teams across multiple time zones
EDUCATION AND EXPERIENCE
- Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related discipline is preferred
- A master’s degree or relevant research experience is beneficial but not required
- Equivalent professional experience, independent technical work, or a strong project portfolio will be considered
- Technical ability, learning speed, judgment, and ownership matter more than a fixed number of years of experience
WHAT THE ROLE OFFERS
- Opportunity to work on technically challenging AI-evaluation and reinforcement-learning problems
- Direct collaboration with research teams, technical customers, and data partners
- Significant ownership over projects and technical delivery
- Remote work with substantial overlap with San Francisco or Singapore
- Competitive annual compensation of $100,000–$200,000
- Potential relocation and visa support for exceptional candidates
HIRING PROCESS
- Application and résumé review
- Initial screening discussion
- Two technical interviews
- Two-to-three-day structured work trial
- Final decision and offer
Kadir.ai is an equal opportunity recruiting partner. Qualified applicants will be considered without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other status protected by applicable law.
Pay: $100,000.00 - $200,000.00 per year
Work Location: Remote
Salary Context
This $100K-$200K range is below the median for Research Engineer roles in our dataset (median: $191K across 47 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 8,943 AI roles we're tracking, Research Engineer positions make up 3% of the market. At Kadir, 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 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 40% below the category median. Disclosed range: $100K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Kadir AI Hiring
Kadir has 2 open AI roles right now. They're hiring across Research Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.