Clarity AI - Staff AI Engineer
Requirements
• Applied MLE Background: You have a proven track record of shipping Machine Learning functionality in a product-focused environment. You prefer "what works in practice" over "what works in theory." • Applied MLE Background: • Bleeding-Edge Awareness: You are a "first adopter" of new AI technologies. You are intimately familiar with the trade-offs between frontier models and know how to swap or hybridize them for maximum impact. • Bleeding-Edge Awareness: • Experimental & Analytical Mindset: You understand the importance of controlled experiments. You know how to design a benchmark, create a "Gold Dataset," and use data to prove that a new prompt or model is an actual improvement. • Experimental & Analytical Mindset: • Practical LLM Expertise: Deep, hands-on experience with LLM orchestration, vector databases, and evaluation frameworks. • Practical LLM Expertise: • Technical Stack Mastery: Expert-level Python and experience writing production-grade code. • Technical Stack Mastery: • Python • Product Mindset: You think about the user. You can identify when a model’s behavior might be technically correct but results in a poor user experience, and you know how to iterate to improve it. • Product Mindset: • Experience: 5+ years of experience in ML or Software Engineering roles, with at least 2+ years of hands-on experience building and scaling GenAI/LLM-powered features. • Self-starter, able to take ownership and initiative, with high energy and stamina • Decisive and action-oriented, able to make rapid decisions even when they are short of information • Highly motivated, independent and deeply passionate about sustainability and impact • Excellent oral and written English communication skills (minimum C1 level-proficient user) • Experience in a start-up • Cloud AI Ecosystem: Familiarity with managed GenAI platforms and services such as OpenAI, Anthropic, AWS Bedrock, or GCP Vertex AI. • Cloud AI Ecosystem: • Active Builder Credentials: A portfolio or GitHub showcasing side projects that experiment with the latest AI paradigms (e.g., multi-agent systems, local LLM execution, etc.). • Active Builder Credentials:
Responsibilities
• As a Staff AI engineer, you will be responsible for: • Product-Centric Development: Designing and executing experiments to improve GenAI capabilities. This isn't just about "accuracy" in a vacuum—it's about optimizing for user value, reliability, and cost-effectiveness. • Product-Centric Development: • Evaluation Systems: Building the "Golden Path" for quality. You will design and implement robust, multi-dimensional evaluation suites (e.g., using "LLM-as-a-judge," semantic checks, and unit tests) to ensure our features are production-ready and hallucination-resistant. • Evaluation Systems: • Advanced RAG & Reasoning Optimization: Moving beyond "naive RAG." You will implement and tune advanced retrieval strategies (e.g., hybrid search, reranking, agentic retrieval) and optimize complex reasoning loops (e.g., CoT, ReAct) to make our current and future agents smarter and more reliable. • Advanced RAG & Reasoning Optimization: • Production-Grade Model Tuning: Leading the strategy for when, and if, to move beyond simple prompting. You will oversee supervised fine-tuning (SFT) and Parameter-Efficient Fine-Tuning (LoRA) workflows to adapt models to our specific product domains. • Production-Grade Model Tuning: • Performance & Cost Engineering: Balancing the "Quality-Cost-Latency" triangle. You will find ways to maintain high-quality outputs while optimizing token usage and reducing inference latency. • Performance & Cost Engineering: • Location
Benefits
• Competitive compensation, both in terms of base salary as well as equity plans that enable to you to share in our success • Flexibility in ways of working both in terms of your schedule as well as your location, whether you prefer to work from home, the office, or abroad with access to a global network of co-working spaces • Flexibility • Generous paid time off schemes, including vacation, sabbatical, religious observance and compensation days • Generous paid time off schemes • Meaningful benefits including private healthcare coverage, fitness and wellness programs covered through Wellhub, working-from-home allowances to help you set up your home office and cover monthly expenses • Professional development with annual training budget for conferences, courses, certifications and access to top market e-learning platforms • Professional development • Collaborative environment with multiple offices around the globe, regular team activities and events as well as employee-led resource groups • Collaborative environment
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