Applied AI Engineer
Job description
About the role
This role builds and operates AI systems that power marketplace matchmaking, ranking, and automation. The position ensures end-to-end reliability for LLM features used by recruiters and companies. Success is defined by measurable business outcomes in production, not offline evaluations alone. You will own the design, implementation, and monitoring of production-grade AI features that directly influence matching quality and automation. The role requires close collaboration with product, engineering, and business stakeholders to translate marketplace needs into robust AI solutions. You will be responsible for maintaining system performance, reliability, and scalability as the platform grows. Your work will drive measurable improvements in candidate-company alignment and marketplace efficiency.
Key facts
What you'll do
Production systems handle matchmaking, ranking, and automation across the Paraform marketplace, enabling efficient candidate-company alignment. Frameworks for real-world evaluation measure performance, reliability, and business impact to validate production behavior. The standard for AI integration across product experiences is elevated through concrete improvements and execution. You will design and implement data pipelines to support real-time ranking and matchmaking workflows. You will partner with cross-functional teams to define metrics that capture business value and user outcomes. You will build and maintain monitoring tools to ensure system reliability and to detect regressions early. You will translate ambiguous marketplace problems into structured experiments and analytical approaches. You will contribute to architectural decisions that balance performance, cost, and scalability. You will communicate findings and tradeoffs clearly to both technical and non-technical audiences.
Requirements
Candidates bring 2-5 years of experience at a growing, AI-native startup across series A to D stages. Direct experience building LLM-powered applications, retrieval systems, tool-using agents, or AI-driven automation is expected. Professional history includes products that scale to large user populations beyond a single enterprise customer. Strong proficiency in Python and Typescript is required, with demonstrated ability in agentic systems that deliver real business or user outcomes. Comfort with ambiguous, 0 to 1 problem spaces is necessary, alongside the capacity to explain technical tradeoffs to non-technical stakeholders. Familiarity with traditional ML techniques such as ranking, recommendation, or classification supports designing hybrid systems that balance performance, cost, and reliability. You must have a strong grasp of data fundamentals, including query design, data modeling, and validation techniques. Experience with SQL and at least one modern data stack is essential for working with production data platforms. You should be comfortable working in fast-paced environments where priorities shift based on business needs. The ability to work independently while aligning with distributed teams is critical for success. A portfolio of shipped analyses or systems that demonstrate impact is valued more than academic background.
Practical notes
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
General applied AI roles focus on deploying models into production environments where reliability and cost-awareness matter. Tooling commonly includes LLM frameworks, retrieval libraries, and agent orchestration platforms. Evaluation practices blend offline metrics with online business signals to validate impact. Teams often iterate rapidly when operating in fast-moving, AI-native marketplaces. Clear communication bridges technical implementations and partner expectations.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.
About the company
Paraform is a marketplace modernizing one of the largest and most fragmented markets in the world: hiring. We partner with industry leaders like Cursor, Palantir, Windsurf, Decagon, Shopify, Coinbase, and Hightouch to hire talent, and are growing quickly - 8×-ing revenue last year.