AI Native Staff Engineer

You've built agentic systems. You've shipped LLM-powered features to production. You know the difference between a cool demo and something that actually works at scale.

What You’ll Do Build AI-Native Product Systems

Design and ship AI-powered product features end-to-end

Architect systems where LLMs, agents, and traditional software work together

Implement RAG pipelines, structured reasoning flows, and tool-using agents

Continuously improve reliability, latency, and cost efficiency

Engineer With AI at Full Leverage

Use AI agents to accelerate development, testing, and architecture decisions

Prototype rapidly and ship production-grade systems

Set up internal AI tooling that multiplies team output

Push the boundary of what’s possible with current models

Own Impact, Not Experiments

Translate product problems into scalable AI-powered solutions

Measure real‑world performance (accuracy, business impact, UX)

Optimize for production robustness — not just demo quality

Shape Our AI‑Native Engineering Culture

Raise the bar for how we use AI internally

Establish pragmatic standards for evaluation and iteration

Mentor engineers on AI-native workflows

Contribute to long-term technical direction

What We’re Looking For You:

Actively use LLMs in your daily workflow

Have built agents, RAG systems, or AI-powered tools

Care about practical reliability over theoretical elegance

Think in orchestration, not just prompts

Experiment constantly with new models and tools

We care less about:

Academic ML background

Publishing papers

Training models from scratch

We care more about:

What you’ve shipped

How you use AI to move faster

How you think about systems that include AI components

Why Join Us We’re redefining recruitment with AI at the core of the product — not bolted on.

You’ll:

Work directly with experienced SaaS leadership (15+ years building in this space)

Have real ownership over architecture and AI direction

Built in a high-velocity, high-impact environment

Help define what AI-native enterprise software looks like

What’s in It for You

Very competitive compensation package

Meaningful stock options

Top-tier tools (MacBook Pro + AI tooling budget)

Hybrid setup + occasional travel

Really make an impact

Staff-Level Engineering Depth

8+ years of professional software engineering experience

Proven track record in designing and shipping large-scale production systems

Experience owning architecture across services or product domains

Strong backend engineering fundamentals (APIs, distributed systems, data modeling, concurrency, reliability)

Experience operating systems in production (monitoring, incident handling, performance tuning)

Cloud-native experience (GCP, AWS, or Azure)

Production AI Systems (Not Research)

Experience shipping LLM-powered features to real users in production

Designed and implemented RAG systems in production environments

Built or architected AI agents or tool-using multi-step reasoning systems

Designed evaluation frameworks for LLM output quality, safety, and regression detection

Experience optimizing AI systems for latency, cost efficiency, and reliability

Experience integrating vector databases and embedding pipelines into scalable systems

AI-Native Engineering Approach

Actively use AI tools and agents to augment your engineering workflow

Demonstrated ability to design systems where AI components and deterministic systems work together

Experience turning fast AI prototypes into production-grade systems

Strong judgment around when to use AI vs deterministic logic

Ownership & Impact

Experience leading complex technical initiatives end-to-end

Ability to translate ambiguous business problems into system architecture

Experience mentoring senior engineers or setting technical standards

Track record of shipping high-impact features with measurable outcomes

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