In this role, you’ll design and scale contextualization, personalization, and reinforcement learning systems that power SQOR.ai’s analytical engine. Your work will directly influence how our Artificial Intelligence interprets business signals, reasons across data, and delivers high-quality insights and recommendations - improving relevance, precision, and measurable impact across user interactions.

This position demands both deep theoretical grounding and practical execution. You’ll work across advanced machine learning, contextual retrieval systems, reinforcement learning loops, and agentic architectures to ensure our AI continuously improves in accuracy, coherence, and decision usefulness within complex business environments.

About SQOR.ai

SQOR.ai is an AI-native Decision Intelligence platform that transforms business data into real-time, actionable insights — without infrastructure, dashboards, or data teams.

Our platform connects directly to the systems companies already use, automatically extracting and analyzing 800+ KPIs to deliver instant answers, predictions, and recommendations . Through our patented Execution Score Algorithm , SQOR.ai quantifies company health and performance across all operations, giving leaders and investors clear visibility — instantly.

We’re redefining how organizations make decisions by replacing the bloated legacy BI stack with an intelligent, adaptive layer of analysis that learns, predicts, and guides execution — in minutes, not months. We have a cosell partnership with Google and are building deeply within the Google Cloud ecosystem.

🎥 Watch reel Google made announcing our partnership:

https://youtu.be/cu1YR2dRuUg?si=m0N_Gs59luwCkzN0

Compensation

The role offers approximately 1% to 2% of company equity for senior level engineers, based on your involvement and contribution. This position represents a commitment to grow alongside our team of 18+ A players all here for equity. As we gain traction and raise capital (expected Q2), salaries will begin to become available.

Key ResponsibilitiesElevate Contextual Intelligence & Personalization

• Design and implement contextual modeling frameworks that improve relevance, personalization, and coherence of AI outputs.
• Develop user-level and organization-level adaptation systems that align insights with goals, performance patterns, and operational signals.
• Build structured context capture and retrieval mechanisms.
• Partner closely with the Chief Data & AI Officer on continuous improvement of measurable response quality.

Advance Machine Learning for Analytical Systems

• Design and optimize machine learning algorithms for scoring, forecasting, anomaly detection, causal inference, and trend analysis.
• Develop reinforcement learning and adaptive optimization strategies that improve system performance over time.
• Integrate structured analytical outputs with AI-driven reasoning layers.
• Contribute to the evolution of quantitative modeling frameworks within the platform.

Develop Agentic AI & Reasoning Architectures

• Architect and improve agentic AI systems that combine structured analytics, contextual memory, and reasoning workflows.
• Design feedback mechanisms that enhance recommendation quality and contextual alignment.
• Improve system capability to respond effectively to open-ended, decision-oriented queries.
• Collaborate cross-functionally to ensure agent behavior aligns with business objectives and measurable outcomes.

Optimize Vectorization & Contextual Retrieval

• Design embedding and vectorization strategies to improve semantic retrieval and contextual alignment.
• Advance hybrid retrieval pipelines that combine structured data and contextual inference.
• Improve memory efficiency and retrieval precision across analytical workloads.
• Work with vector databases and matching engines in production environments.

System Performance & AI Infrastructure

• Optimize inference quality, latency, and contextual coherence at scale.
• Deploy and tune AI workloads within Google Cloud and Vertex AI environments.
• Collaborate with infrastructure engineers to support scalable ML pipelines and distributed inference.
• Ensure AI systems continuously evolve alongside expanding data coverage and use cases.

Required Skills & Experience

• 5+ years designing and optimizing Artificial Intelligence and machine learning systems in production environments.
• Deep expertise in contextual modeling, personalization systems, and reinforcement learning.
• Strong grounding in statistical modeling, causal inference, and quantitative analytics.
• Advanced proficiency in Python and modern AI frameworks.
• Experience building agentic systems, retrieval-augmented pipelines, or contextual reasoning architectures.
• Experience with vector databases and embedding systems.
• Production experience within Google Cloud; Vertex AI experience strongly preferred.
• Strong systems thinking across ML, NLP, and distributed architectures.

Preferred Qualifications

• Experience improving measurable decision quality in AI systems.
• Background in applied AI research or advanced contextual modeling.
• Experience with reinforcement learning optimization techniques.
• Familiarity with Decision Intelligence, analytics automation, or enterprise AI systems.
• Experience building AI systems that generate structured, decision-oriented outputs.

Why Join Us?

• Help shape the intelligence architecture powering one of the most advanced Decision Intelligence platforms in the market.
• Work at the frontier of contextual AI, agentic systems, and enterprise analytics.
• Join a fast-moving, remote-first team driven by innovation and measurable impact.
• Competitive equity, long-term upside, and the opportunity to influence how Artificial Intelligence delivers real business value.