AI Innovation Engineer, Office of the CEO

Optimove is a global marketing tech company, recognized as a Leader by Forrester and a Challenger by Gartner. We work with some of the world's most exciting brands, such as Sephora, Staples, and Entain, who love our thought-provoking combination of art and science. With a strong product, a proven business, and the DNA of a vibrant, fast-growing startup, we're on the cusp of our next growth spurt. It's the perfect time to join our team of ~500 thinkers and doers across NYC, LDN, TLV, and other locations, where 2 of every 3 managers were promoted from within. Growing your career with Optimove is basically guaranteed.

Are you passionate about AI and machine learning? Do you thrive on rapid experimentation, prototyping, and building innovative tools that solve real business problems?
Do you enjoy diving into unfamiliar technologies, quickly assessing and demonstrating their potential impact?
We're looking for an exceptional AI Innovation Engineer to join the Office of the CEO—someone who can seamlessly blend deep technical expertise with product thinking and business acumen. In this high-impact role, you'll lead AI initiatives that drive both internal efficiency and product innovation, working closely with the CEO, R&D, and Product teams.
You'll take ownership of architecting and building fast-moving PoCs, exploring new technologies, and developing custom solutions that go beyond standard integrations to deliver real business impact.

As an AI Innovation Engineer, you will:
Rapidly design and develop PoCs using cutting-edge AI and ML technologies.
Lead the design and development of AI-powered tools that streamline internal processes and boost productivity.
Build innovative AI features into Optimove's product suite, focusing on tangible customer and business impact.
Explore and evaluate new frameworks, tools, and approaches—without being limited to familiar stacks.
Partner closely with Product teams to deeply understand use cases, define requirements, and shape AI-driven solutions.
Work hand-in-hand with R&D and Engineering teams to bring AI concepts to life—from ideation to production.
Take ownership of complex technical challenges, architecting AI solutions that are scalable, maintainable, and robust.
Act as a trusted advisor to the CEO, providing insight into the strategic application of AI across the business.
Research, evaluate, and implement emerging AI technologies—prioritizing in-house development of tailored solutions where it creates strategic advantage.
Bridge technical and business worlds, ensuring AI initiatives align with overall company objectives.
Requirements:
3+ years of experience in software engineering, with a proven track record of delivering fast, functional PoCs across diverse technologies.
Ability to quickly assess, prototype, and demonstrate emerging technologies, even without prior hands-on experience.
Proven experience designing, building, and deploying AI-powered tools and frameworks—not just integrating third-party APIs.
Strong understanding of machine learning algorithms, LLMs, and AI system design.
Product- and business-oriented mindset: ability to translate business needs into AI-driven product features and real-world solutions.
Proficiency in Python and experience with AI/ML libraries (TensorFlow, PyTorch, Hugging Face).
Deep familiarity with AI APIs (OpenAI, Google AI, Azure AI), plus flexibility to experiment with unfamiliar tools.
Strong collaboration skills, working closely with Product and Engineering teams.
Fluent in English, with excellent communication skills for cross-functional collaboration and leadership visibility.
Nice to Have:
Experience working in innovation labs or R&D environments.
Experience with cloud platforms (AWS, GCP, Azure) and scalable AI infrastructure.
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for AI deployments.
Knowledge of MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow).
Experience with vector databases, RAG pipelines, and knowledge management systems.
Prior experience translating PoCs into productized features or guiding teams through the transition from prototype to production.
Open-source contributions or research publications in AI/ML.