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Research Engineer / Scientist
Location: United States
Employment Type: Full-time
Focus: Frontier AI, Agentic AI, Reinforcement Learning, Computer Use, Multimodal Models, Long-Horizon Agents
About Our Client
Our client is a stealth-stage AI company building frontier models focused on human intent understanding, computer use, and autonomous agent systems.
The founding team includes leaders from Tesla AI, Google DeepMind, NVIDIA, Physical Intelligence, and Apple. They are building from the ground up and looking for early research talent to help shape the technical direction, research agenda, and product architecture from day one.
This is an opportunity to work on some of the hardest problems in applied AI: teaching agents to understand how people work, represent intent, use computers, and complete long-horizon tasks with increasing autonomy.
About the Role
Our client is hiring a Research Engineer / Scientist to own ambitious research bets end to end.
This person will work across hypothesis generation, data strategy, model training, evaluation, deployment, and iteration. The role is focused on reinforcement learning, post-training, continual learning, multimodal agents, and long-horizon autonomous systems.
The team is open to a range of backgrounds, from fresh PhD graduates with strong research internships to senior researchers who have led teams at top AI labs.
What You’ll Do
- Run experiments and train frontier AI models focused on human judgment scaling, computer use, and intent representation learning
- Post-train LLMs and multimodal agents using reinforcement learning and continual learning methods
- Work with large-scale screen recording and behavioral data to understand how individual users work
- Build models that can learn user workflows and proactively automate tasks
- Design and execute rigorous research experiments that improve autonomous agent capabilities
- Own research bets end to end, from hypothesis and data through training, evaluation, deployment, and measurement
- Help define the company’s research direction as an early technical team member
- Partner closely with founders and engineering leadership on technical architecture and product strategy
- Translate cutting-edge research into systems that can power real product experiences
What We’re Looking For
- Experience in frontier AI research, applied AI research, or research engineering
- Strong background in machine learning, deep learning, reinforcement learning, post-training, continual learning, or multimodal systems
- Experience designing and running rigorous experiments
- Ability to move from research idea to implemented system
- Strong programming and engineering fundamentals
- Comfort working with large-scale datasets and complex model training workflows
- Strong judgment around model evaluation, data quality, experimental design, and deployment readiness
- Ability to operate in ambiguity and take ownership of open-ended research problems
- Clear communication and ability to collaborate with a small, high-caliber founding team
- Excitement about computer use, autonomous agents, human intent modeling, and the future of AI-native software
Relevant Research Areas
Relevant experience may include:
- Reinforcement learning
- Post-training
- Continual learning
- Long-horizon agents
- Computer use agents
- Multimodal LLMs
- Human behavior modeling
- Intent representation learning
- Human judgment scaling
- Behavioral data modeling
- Agent evaluation
- AI automation
- Large-scale model training
Ideal Background
Our client is open to a range of seniority levels, including:
- Fresh PhD graduates with strong research internship experience
- Research engineers from frontier AI labs
- Applied scientists with experience training and evaluating advanced AI systems
- Senior researchers who have led teams or major technical efforts
- Builders who can connect research quality with production-minded execution
Why This Opportunity
- Join a stealth-stage AI company at the founding team stage
- Work directly with leaders from Tesla AI, Google DeepMind, NVIDIA, Physical Intelligence, and Apple
- Help define the research agenda from day one
- Own ambitious technical bets across reinforcement learning, post-training, continual learning, and agentic AI
- Work on frontier problems in computer use, human intent understanding, and long-horizon autonomous agents
- Build models that learn how people work and help automate real tasks
- Shape both technical architecture and product direction early
Ideal Candidate Profile
The ideal candidate is a research-minded builder who wants to push the frontier of agentic AI.
They can design strong experiments, train models, reason deeply about evaluation, and turn ambiguous research questions into working systems. They are excited by the challenge of building AI agents that understand human intent, use computers effectively, and improve through real-world interaction.
This person wants to help shape the research foundation of a company from the earliest stage.
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 NxT Level, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200.
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.
NxT Level AI Hiring
NxT Level has 1 open AI role right now. They're hiring across Research Engineer. Based in San Francisco, PR, US.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% above the national median.
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.
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