Recommendation Systems Engineer (Senior / Strong Middle)

Flyer One Ventures

1d•Remote

About The Position

An early-stage, AI-first startup is building a fragrance discovery product that helps people find scents based on their preferences, emotions, and desired experiences. The Recommendation Systems Engineer will own how the product understands user preferences and recommends fragrances, working directly with the founders, engineers, and product team to design, build, and improve recommendation systems from the ground up. The team is led by second-time founders with a successful exit and experience at Pinterest. The role covers personalized recommendations, semantic search, similar-fragrance discovery, candidate retrieval, ranking, and re-ranking. It also involves addressing cold-start challenges, building data pipelines and ML-powered services, defining evaluation metrics, running experiments, measuring recommendation quality, making architecture decisions, and taking systems from concept to production. The engineer may also contribute to backend and product engineering. AI coding agents such as Claude Code and Codex are a core part of the development workflow. This role offers substantial ownership over the recommendation architecture and product direction, close collaboration with experienced founders, and direct influence on the user experience. The company values initiative, practical decision-making, and getting work done without micromanagement or excessive approvals. Work is remote, with Warsaw preferred and an office available to local teammates. Equity is offered.

Requirements

Hands-on experience building or improving recommendation systems, search ranking, or personalization in production.
Strong understanding of retrieval, ranking, machine-learning models, and experimentation.
Ability to design and own systems end-to-end, not just individual models.
Solid software engineering and backend development skills.
Experience working with user behavior data and measuring product impact.
Product mindset and ability to make independent technical decisions.
Comfort working in a small team with evolving priorities.
Interest in AI-first engineering and using coding agents in daily development.

Nice To Haves

Experience in e-commerce, marketplaces, consumer products, or discovery platforms.

Responsibilities

Design and build personalized recommendation systems, semantic search, and similar-fragrance discovery.
Develop candidate retrieval, ranking, and re-ranking approaches.
Address cold-start challenges and identify useful user signals.
Build data pipelines and ML-powered services that support recommendations.
Define evaluation metrics, run experiments, and measure recommendation quality.
Make practical architecture decisions and take systems from idea to production.
Contribute to backend and product engineering when needed.
Use AI coding agents such as Claude Code and Codex as a core part of the development workflow.
Help define the recommendation architecture and decide what to build.