We’re looking for an Applied AI Engineer who combines strong ML fundamentals with the discipline of improving production AI systems through metrics, evaluation, and iteration.
This role is hands-on, product-focused, and collaborative with the AI platform lead.
The role in a nutshell
You’ll work on improving production AI systems through evaluation, experimentation, and system design.
A large part of the role involves:
- diagnosing failures in agent workflows
- designing evaluation metrics and KPIs
- improving system prompts and agent behavior
- running structured experiments and measuring impact
You won’t be working in isolation on research projects — you’ll be improving systems that real users depend on.
Rough responsibility breakdown:
- AI evaluation and KPI design — ~30%
- Prompt and agent system design — ~30%
- ML systems (recommendation, optimization, etc.) — ~30%
- Engineering integration — ~10%
What you’ll work on:AI evaluation and system quality
- Design evaluation strategies for LLM and agent workflows
- Create metrics and KPIs for AI system performance
- Build and maintain evaluation datasets
- Debug production AI failures systematically
- Compare system behavior against baselines
This is a core responsibility of the role.
Multi-agent AI systems
- Improve agent orchestration and workflows
- Diagnose failures across agent pipelines
- Refine system prompts and agent interactions
- Improve reliability, latency, and response quality
ML and AI systems
You’ll contribute to areas such as:
- Recommendation systems (ranking and personalization)
- Itinerary optimization and constraint-based planning
- LLM-based reasoning systems
- Optional: computer vision pipelines
Depth in one of these areas is more important than superficial experience in all of them.
Engineering collaboration
We use:
- Golang (primary production language)
- Python when necessary for ML workflows
- Postgres, Redis, and internal services
You don’t need to be a Go expert on day one, but you should be comfortable reading and modifying production code.
Backend engineers handle infrastructure-heavy service development — your focus is AI system behavior and correctness.
What we’re looking forMust-haves
Strong AI/ML fundamentals You understand the theory behind what you build and can choose appropriate methods for a problem.
Examples:
- evaluation metrics (precision/recall/F1/etc.)
- ranking and recommendation concepts
- embeddings and similarity
- experimentation methodology
Not required:
- academic publications
- advanced theoretical math
- large-scale model training experience
Evaluation-driven mindset You:
- think in metrics and baselines
- design experiments instead of guessing
- measure system improvements quantitatively
- debug failures methodically
This is the most important signal for the role.
Experience with LLM systems You’ve worked with:
- prompt design
- agent workflows
- evaluation of LLM outputs
- production LLM integrations
Ability to ship production systems You can:
- turn ideas into working systems
- iterate based on results
- balance exploration with delivery
Programming ability You’re comfortable writing production code in at least one language (Python, Go, or similar) and learning others when needed.
Strong signals (nice to have)
- Experience improving an AI system after deployment
- Recommendation systems or ranking experience
- Optimization or constraint-based systems
- Computer vision experience
- Experience building evaluation frameworks
- Golang experience
- Startup or small-team engineering experience
This role may not be a fit if
- You are looking for a research focused role without production deployment
- You rely heavily on frameworks without understanding fundamentals
- You’re uncomfortable working with partially-defined problems
- You prefer narrow specialization over product ownership
