Hire AI Research Engineersin 2 weeks
BearPlex AI research engineers bridge research and production: implementing recent papers, designing novel architectures, evaluating frontier capabilities, and turning research insights into production systems. For teams pushing the boundary of what AI can do for them.
What a AI Research Engineer actually does at BearPlex
An AI research engineer at BearPlex sits between academic research and production deployment. The role spans: implementing recent papers (turning arXiv preprints into working production systems), designing novel architectures for problems that don't have off-the-shelf solutions, evaluating frontier model capabilities for emerging use cases, leading research-heavy phases of complex AI engagements, and translating research insights into production systems. They've worked across the full spectrum: fine-tuning research, RLHF / DPO / Constitutional AI, agent system design, novel retrieval architectures, multi-modal AI, reasoning systems. They read papers continuously, implement the most-promising recent work, and have the engineering skill to take research from notebook to production. This is a rare profile; most engineers don't read papers continuously, and most researchers don't ship production systems.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Implemented 8 recent research papers in production for client engagements: from Constitutional AI variants to advanced reasoning patterns. Reduced research-to-production time from months to weeks.
Designed novel multi-agent reasoning architecture for a Series C client: combined techniques from 3 recent papers into a custom architecture that outperformed off-the-shelf solutions on the target task.
Lead alignment research engineer for client work: implemented production DPO + Constitutional AI variants that improved model behavior on safety-critical tasks.
Senior research engineer for multimodal AI work: implemented and adapted recent multimodal architectures for client production deployment in regulated industries.
Skills matrix
The capabilities every BearPlex AI Research Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Recent paper implementation (production-quality) | Expert | PyTorch · JAX · Hugging Face · research code adaptation |
| Novel architecture design | Expert | Custom architecture design · Empirical methodology |
| Frontier capability evaluation | Expert | Capability benchmarking · Eval design · Research-grade rigor |
| Distributed training (large-scale fine-tuning) | Expert | DeepSpeed · FSDP · Megatron-LM · Multi-GPU orchestration |
| Alignment research (RLHF, DPO, CAI) | Expert | TRL · OpenRLHF · Custom alignment pipelines |
| Multi-agent and reasoning research | Expert | LangGraph · Custom multi-agent orchestration · Reasoning evaluation |
| Multimodal AI (text + image + audio + video) | Advanced | CLIP variants · Vision-language models · Multimodal architectures |
| Research methodology and experimental rigor | Expert | Statistical testing · Ablation design · Reproducibility practices |
| Research-to-production translation | Expert | Production engineering for research code · Performance optimization |
| Paper writing and technical communication | Advanced | Technical documentation · Research write-ups |
| Cross-disciplinary research (NLP + CV + RL) | Advanced | Multi-domain technique transfer |
| Open-source community engagement | Advanced | GitHub contribution · Hugging Face Hub · Research community |
How we vet AI research engineers
Research interview
60-minute deep-dive on past research engineering work. We probe: which recent papers have they implemented and what did they learn? Can they critique a recent paper in detail? Have they shipped research-derived systems to production? We screen out engineers whose 'research' was purely academic without production translation.
Live paper implementation exercise
We give the candidate a recent paper (last 12 months) related to LLMs, agents, or alignment, and 2 hours. They must understand the paper enough to discuss design decisions, implement a small part of the technique, and evaluate critically. We're looking for: paper comprehension, implementation skill, critical thinking.
Architecture interview
Whiteboard a novel AI system for a problem without off-the-shelf solutions. We probe for: ability to combine techniques from multiple research areas, empirical thinking, awareness of limitations, production translation considerations.
Hamad-led trial engagement
Trial engagement on a real client research problem, supervised directly by Hamad Pervaiz. Research engineering quality is hard to assess in interview; the trial proves it.
What clients say
“Their research engineer implemented a novel multi-agent reasoning architecture from recent papers that outperformed every off-the-shelf solution we tried. The work directly enabled a product feature we couldn't otherwise have shipped.”
“Best research-to-production engineer I've worked with. He read recent papers continuously, implemented the most-promising ones, and translated them into production-quality systems. Force multiplier for our internal research team.”
“We needed someone who could implement a Constitutional AI variant for our specific safety requirements. The BearPlex research engineer designed and shipped it in 4 weeks: faster than we'd thought possible.”
Hiring AI research engineers: questions answered
Sometimes, but it's not the primary deliverable. Our research engineers focus on translating research into production for client work, not publishing academic papers. Some have publications from prior roles; current work is client-confidential and typically doesn't result in publications.
Hire a research engineer when (1) your problem doesn't have an off-the-shelf solution and requires novel work, (2) you want to implement recent research techniques, (3) you need someone who can critique and adapt the latest papers. Hire an AI engineer when the problem is well-understood and execution is the priority.
Yes: increasingly common engagement type. We've implemented production RLHF, DPO, and Constitutional AI variants for clients with specific safety or alignment requirements. This is research-engineering work that requires both alignment-specific knowledge and production engineering skill.
Embedded research engineer: $30K-$50K monthly retainer (typically 3-12 months). Per-research-project engagement: $80K-$300K depending on scope (typically 8-16 weeks). Research engineering is more expensive than typical engineering due to the senior profile and specialized skill.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest.
Yes: common pattern. The research engineer joins your internal research team, contributes to their roadmap, leads specific research projects, and helps translate research output into production. Acts as a force multiplier for existing internal research function.
Deepest expertise in: LLM fine-tuning and alignment (RLHF, DPO, CAI), production agent systems (multi-agent, reasoning), retrieval research (advanced RAG architectures), and multimodal AI. For other research areas (RL, robotics, time-series), we staff per the specific engagement requirements.
Related services
Featured case studies
Get matched with a AI Research Engineer in 14 days
21-day risk-free trial. We've placed engineers at Fortune 500s and high-growth scale-ups.