Applied AI Engineer (AI-Native Marketplace)

Applied AI Engineer (AI-Native Marketplace)

Location: New York City (on-site, relocation supported) Compensation: USD 170,000 – 230,000 + Equity Hiring: Up to 2 engineers Company: Confidential (represented by Worthland)

About the Project

Our client is a well-funded, AI-native startup building a real production marketplace in a space that has historically relied on manual workflows and human judgment.

The company has achieved clear product–market fit, is growing rapidly with a lean team, and uses AI not as an add-on but as the core decision engine of the business. Their systems directly impact matching quality, speed, and operational efficiency at scale.

This is a hands-on role for builders who want to ship AI systems that operate in messy, real-world conditions and materially move business metrics.

The Role

As an Applied AI Engineer, you will own end-to-end AI systems powering the marketplace: scoring, matching, ranking, recommendations, and internal automation.

You will work directly with the founder and operations team. There is no separate ML platform team—you own the full loop, from problem definition through production iteration.

This is not a research role and not an infra-only ML position. It is a product-focused, applied AI builder role.

Role Split (Approximate)

• ~60% Applied AI / ML: model selection, prompting, fine-tuning, evaluation, experimentation

• ~40% Product & Engineering: backend services, APIs, data pipelines, product integration

First 30 Days (What Success Looks Like)

Week 1

• Develop a deep understanding of existing AI systems (scoring, matching, automation)

• Identify where models perform well vs. where they fail in production

• Partner with the ops team to understand manual overrides and edge cases

• Align with the founder on the highest-leverage problems to solve first

Weeks 2–4

• Ship meaningful improvements to scoring/matching models and related product features

• Begin reducing manual ops load through smarter automation

• Take ownership of the AI roadmap, using the founder as a thought partner—not a project manager

Key Responsibilities

Applied AI & Decision Systems

• Build and improve matching, scoring, and ranking systems that directly affect marketplace outcomes

• Design explainable and calibratable AI outputs so users can understand and trust decisions

• Compare new inputs against historical outcomes to surface meaningful signal

• Iterate based on real-world feedback, not just offline metrics

Automation & Internal Systems

• Replace manual review steps with intelligent AI-driven workflows

• Build feedback loops where human overrides improve model performance over time

• Identify and surface stuck or anomalous cases requiring human attention

Product-Facing AI Features

• Generate structured summaries from unstructured data (documents, notes, transcripts)

• Build retrieval-based assistants and role-specific AI tools

• Ship features that are user-facing, measurable, and tightly integrated into the product

Experimentation & Evaluation

• Design evaluation frameworks that reflect real business impact, not just model accuracy

• Implement experimentation and A/B testing to validate improvements

• Make pragmatic tradeoffs (prompt vs. fine-tune, off-the-shelf vs. custom)

Requirements

Must-Have

• 4+ years of engineering experience with AI/LLM features shipped to production

• Experience owning AI systems end-to-end in a real product environment

• Hands-on experience with LLM APIs, embeddings, vector databases, and evaluation

• Strong backend or full-stack engineering fundamentals

• Comfort working with imperfect data and evolving requirements

Strong Plus

• Experience at an AI-native startup or as a founding / early engineer

• Background in recommendation systems, matching, search, or automation

• Product intuition and comfort making fast, high-impact decisions

• Willingness to be hands-on rather than operating through layers of abstraction

Not a Fit If You Primarily:

• Work only on ML platforms or infrastructure without owning product features

• Focus on research, publishing, or offline experimentation

• Require clean datasets and mature infra before shipping

• Have only built basic RAG demos as your main AI experience

• Prefer large, highly structured organizations

Why This Role

• High ownership and direct impact on core business metrics

• Close collaboration with the founder and decision-makers

• AI-first product where models are central, not decorative

• Competitive compensation, meaningful equity, and long-term upside

Interview Process

• Intro Call (30 minutes): High-level screening to assess role fit, hands-on AI experience, and mutual interest in the opportunity and company stage.

• Technical Interview (60 minutes): Applied system design discussion focused on a real-world AI problem (e.g. matching, ranking, automation), evaluating practical decision-making and tradeoffs.

• Behavioral Deep Dive (60 minutes): Review of past experiences, ownership mindset, and working style in high-autonomy, fast-moving environments.

• On-Site (Full Day): Collaborative working session solving real problems with the team to assess technical depth, problem-solving approach, and collaboration in practice.