Senior AI Engineer

What You Will Work On

• Drop into business functions to identify where AI has the highest leverage, define what to build, and own the full arc from scoping through handoff
• Spot and resolve AI-specific systems gaps across the org - missing shared infrastructure, fragmented adoption patterns, absent measurement benchmarks - and move on them faster than the rest of the org could
• Scan externally for emerging AI capabilities and market patterns; translate signal into written briefs that inform what the team experiments on next
• Build AI-powered tools and pipelines using frontier models, agent frameworks, and LLM infrastructure as core building blocks, not experiments
• Design and implement evaluation frameworks (evals) to measure whether AI systems are performing as intended — not just technically functional but meaningfully useful
• Instrument AI systems for observability: logging, tracing, cost tracking, latency monitoring, and anomaly detection using tools like Datadog, LangSmith, or equivalent
• Navigate cross-functional ownership dynamics: identify the right stakeholders, build alignment, and hand off in a way that sticks
• Represent AI Frontiers in cross-functional forums and executive reviews as a credible voice on what AI can and can't do

Qualifications

• 6+ years of professional hands-on software engineering experience
• Full-stack or backend engineering depth - shipped and maintained production systems, not just prototypes
• 2+ years, hands-on experience building with LLMs and AI tooling in production: agent frameworks, pipelines, retrieval systems, or AI-integrated workflows that real users depend on
• Experience designing and running evals: you know how to define what "working" means for an AI system and build the measurement scaffolding to prove it
• Familiarity with LLM observability tooling (LangSmith, Datadog LLM monitoring, or equivalent): you instrument what you build and use signal to improve it
• Track record of working across functions: partnered with non-engineering stakeholders, understood their problem domain, built something that fit
• Strong written and verbal communication with non-technical audiences — you can explain a technical tradeoff to an ops lead without losing them
• Comfort with ambiguity: you scope your own work, write your own briefs, and don't need a detailed spec to get started

Nice to Have

• Practical machine learning experience: familiarity with model behavior, data quality, iteration loops, and how those fundamentals translate into building reliable AI systems

Compensation

• The base wage range for this position based in our New York City Office is targeted at $188,000.00 - $242,000.00 per year.