Applied AI Engineer (AI-Native Marketplace)
| Verified Pay check_circle | Provided by the employer$170000 - $230000 per year |
|---|---|
| Hours | Full-time |
| Location | New York, NY New York, New York open_in_new |
About this job
Job Description
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)
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 RoleAs 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
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
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
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
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
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)
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
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
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
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
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.
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