Applied AI Engineer

Fieldcrest Ventures • Full-time • Nashville, TN, US • $150k - $200k / year • 1m ago

Why Fieldcrest

Fieldcrest Ventures is a venture builder and management company that actively operates AI-driven transformations across our portfolio. At Fieldcrest Ventures, we embed ourselves as operators, applying frontier AI capabilities to transform service businesses into software companies. Our portfolio companies are deploying cutting-edge AI agent systems across diverse industries, creating unique opportunities for engineers to ship production AI at scale.

Role Overview

As an Applied AI Engineer at Fieldcrest, you will ship AI-powered product features end-to-end across our portfolio companies. You'll turn business problems into working features using foundation models plus production-ready code—not research demos. This role requires someone who can rapidly prototype, build comprehensive evaluation systems, and productionize AI at scale while working across diverse industries and teams.

This is a unique opportunity for an engineer who wants startup variety with institutional backing, direct founder access, and the chance to build AI systems that actually ship to customers.

Why This Role

Direct portfolio impact: Work across multiple startups and growth-stage companies, shipping AI features that transform entire businesses
Proven playbook: Apply and evolve our service-as-software transformation methodology using the latest in AI/LLM technology
Exceptional exposure: Work directly with our CEO and Chief of Staff, embedding with portfolio company teams to drive technical execution
Pattern recognition: Build once, deploy many—create reusable AI infrastructure that accelerates our entire portfolio
Ground-floor opportunity: Join as we scale from managing 2 to 7+ companies, with a path toward deep tech ventures

What You'll Do

Own features end-to-end from problem definition through deployment, instrumentation, and iteration across portfolio companies
Design eval suites, golden sets, and guardrails while measuring quality (accuracy, hallucination, refusal rates), latency, and cost
Select and route between model vendors (OpenAI, Anthropic, open-source), implement RAG pipelines, manage context windows, and build fallback strategies
Instrument features, capture traces, label edge cases, curate training/fine-tuning corpora, and close feedback loops
Work directly with portfolio company founders, PMs, and engineers to translate user needs into model behaviors and UX flows

Requirements

What You Have

3+ years of engineering experience shipping products where ML/AI models are core components
You've taken AI features from prototype to production, not just built demos
Experience building eval frameworks and using data to drive model improvements
Comfortable across Python/TypeScript, vector stores, retrieval pipelines, and standard backend/cloud infrastructure
Thrives in ambiguity, comfortable with rapid context-switching, self-directed execution
Knows when to use cutting-edge vs. boring technology; optimizes for shipping over perfection
Can explain complex AI systems to non-technical stakeholders and influence technical decisions

Nice to have