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.

And yet… somehow you're still spending most of your day writing CRUD endpoints.

We're hiring an AI Native Staff Engineer at Carv. We build agentic AI systems in the recruitment space — and we need someone who's done this before, not someone who's just getting started.

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