Senior Applied AI Engineer
- Compensation
- Not disclosed
- Location
- TLV Office
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- MSc or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of experience in data science or applied machine learning, plus 2+ years in ML engineering or software engineering, with direct responsibility for deploying and maintaining production systems
- Proven ownership of significant AI products or features. You have been a primary technical driver, taking ambiguous problems from initial concept to a working product used by real customers.
- Strong foundations in machine learning and statistics, including experimental design, model evaluation, and practical experience with NLP, embeddings, clustering, or related methods for analyzing unst
- Strong Python, SQL and Typescript skills, alongside solid software engineering practices: modular architecture, automated testing, version control, code reviews, and maintainable production code.
- Hands-on experience building LLM-powered applications or AI agents beyond the prototype stage, including tool calling, structured outputs, context management, and systematic evaluation.
- Experience deploying and operating systems in a cloud environment, including containerization, CI/CD pipelines, logging, monitoring, and debugging production issues.
- Strong product judgment and independent execution. You can define milestones, prioritize experiments, communicate technical trade-offs, and collaborate effectively across product, engineering, and bus
Full description
About Brandlight
Brandlight is the leading enterprise AI visibility platform, built to help the world’s leading brands understand, manage, and optimize how they appear across AI-driven search, commerce, and advertising.
As AI rapidly becomes a trillion-dollar marketing and distribution channel, Brandlight is defining the systems that decide how brands are surfaced, trusted, and chosen.
Join our team and help build the next generation of tools shaping the future of brand management in an AI-first world.
About the Role
We’re looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
You’ll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.
You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. You’ll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
- Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
- Develop and improve our core algorithms.
- Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
- Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices—not just standalone prototypes.
- Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
- Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
- Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.