Senior AI/ML Engineer

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