AI Engineer

Clarity AI; United States; Hybrid; USD 79,926 - 125,599; Full time

Job summary

Clarity AI is seeking an AI Engineer to bridge rapid experimentation and agile product development, shaping our GenAI roadmap and driving proof-of-concept to production.

You will design experiments, build evaluation benchmarks, and lead model tuning and efficiency optimizations to deliver robust, scalable AI features that align with product goals.

Qualifications

• Applied ML in a product-focused environment.
• Strong experimental design and benchmarking.
• Hands-on with LLM orchestration and evaluation.

Responsibilities

• Design and execute experiments to improve GenAI capabilities for product value, reliability, and cost-efficiency.
• Build evaluation suites to ensure features are production-ready and hallucination-resistant.
• Implement and tune retrieval strategies and multi-agent reasoning loops to enhance agent performance.
• Lead strategy for moving beyond prompting with supervised fine-tuning and LoRA.
• Balance quality, cost, and latency while optimizing token usage and inference speed.

Skills

Python

Production-grade code

Experiment design

LLM orchestration

English communication

Tools

Vector databases

Evaluation frameworks

Job description

About the Role

We are looking for an AI Engineer who thrives at the intersection of rapid experimentation and agile product development. In this role, you will bridge the latest AI developments and tangible product impact, helping define our roadmap and driving the quality loop from proof‑of‑concept to production.

What You’ll Be Doing

• Product‑Centric Development: Designing and executing experiments to improve GenAI capabilities, focusing on user value, reliability, and cost‑effectiveness.
• Evaluation Systems: Building the “Golden Path” for quality with multi‑dimensional evaluation suites (LLM‑as‑a‑judge, semantic checks, unit tests) to ensure features are production‑ready and hallucination‑resistant.
• Advanced RAG & Reasoning Optimization: Implementing and tuning retrieval strategies (hybrid search, reranking, agentic retrieval) and complex reasoning loops (CoT, ReAct) to enhance agent performance.
• Production‑Grade Model Tuning: Leading the strategy for moving beyond prompting, overseeing supervised fine‑tuning (SFT) and Parameter‑Efficient Fine‑Tuning (LoRA) to adapt models to specific domains.
• Performance & Cost Engineering: Balancing the “Quality‑Cost‑Latency” triangle to maintain high‑quality outputs while optimizing token usage and reducing inference latency.

Location

The role is based in Spain. Way of Working: Remote / Hybrid.

What You’ll Need

• Applied MLE Background: Proven track record of shipping Machine Learning functionality in a product‑focused environment.
• Bleeding‑Edge Awareness: First adopter of new AI technologies, familiar with trade‑offs between frontier models.
• Experimental & Analytical Mindset: Skilled in controlled experiments, benchmark design, Gold Dataset creation, and data‑driven validation.
• Practical LLM Expertise: Hands‑on experience with LLM orchestration, vector databases, and evaluation frameworks.
• Technical Stack Mastery: Expert‑level Python and production‑grade code.
• Product Mindset: User‑centric approach, identifying when model behavior may be technically correct but produce poor UX.
• Experience: 3+ years in ML or Software Engineering roles, with at least 2+ years building and scaling GenAI/LLM features.
• Self‑starter, ownership, high energy and stamina.
• Decisive and action‑oriented, able to make rapid decisions under uncertainty.
• Highly motivated, independent, and deeply passionate about sustainability and impact.
• Excellent oral and written English communication skills (minimum C1 level).

Nice to Have

• Experience in a start‑up.
• Cloud AI Ecosystem: Familiarity with managed GenAI platforms such as OpenAI, Anthropic, AWS Bedrock, or GCP Vertex AI.
• Active Builder Credentials: Portfolio or GitHub showcasing side projects with multi‑agent systems, local LLM execution, etc.

What We Offer

• Competitive compensation, including base salary and equity plans.
• Flexibility in schedule and location, with options for home, office, or abroad and access to co‑working spaces.
• Generous paid time off with vacation, sabbatical, religious observance, and compensation days.
• Meaningful benefits including private healthcare coverage, fitness and wellness programs, and home‑office allowances.
• Professional development with annual training budget for conferences, courses, certifications, and e‑learning platforms.
• Collaborative environment with offices worldwide, team activities, and employee‑led resource groups.