Senior Fullstack Engineer (Matchmaking)

United StatesRemoteFull-timesenior

EngineeringFullstack Developer

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Quick Summary

Key Responsibilities

Design, develop, and scale an ML-powered matching engine that connects creators with relevant brand campaigns.

Requirements Summary

At least 7 years of software engineering experience, preferably within SaaS environments. Strong proficiency in modern JavaScript technologies, including React, Redux, Node.js, and Express.

Technical Tools

EngineeringFullstack Developer

As a Senior Fullstack Engineer, you will play a key role in building an intelligent, ML-powered matchmaking engine that connects creators with relevant brand campaigns.
You will design and develop scalable web services and recommendation systems using modern fullstack technologies, vector search, and performance data.
Working within a growing, product-focused engineering team, you will contribute to the technical roadmap and turn complex business needs into reliable solutions.
You will take ownership of features throughout their entire lifecycle, from architecture and development to deployment and continuous improvement.
The role combines hands-on engineering, technical leadership, and collaboration across product and engineering teams.
This is an opportunity to shape a data-driven platform while helping establish the technical foundations for its continued growth.

You will contribute to the engineering strategy while building and improving the systems that power creator-brand matchmaking. Your responsibilities will include:

Design, develop, and scale an ML-powered matching engine that connects creators with relevant brand campaigns.
Contribute to the engineering technical roadmap and translate product priorities into effective technical solutions.
Build and maintain scalable web services, high-throughput REST APIs, and application features using modern JavaScript technologies.
Design and improve multi-stage matching and recommendation pipelines, including candidate generation, filtering, and re-ranking.
Integrate vector similarity search and feedback mechanisms that use real-world outcomes to continuously improve match quality.
Support production ML capabilities through reliable feature pipelines, consistent model inputs, and monitoring of model drift and data quality.
Lead the full product development lifecycle for selected features, from technical design and implementation to deployment and maintenance.
Maintain and improve CI/CD processes, engineering standards, coding guidelines, and continuous delivery practices.
Conduct and facilitate code reviews, identifying opportunities to improve architecture, performance, security, and code quality.
Investigate production incidents and diagnose infrastructure and application bottlenecks, including memory, I/O, and CPU constraints.
Identify and introduce tools and processes that improve engineering productivity and team effectiveness.
Communicate project progress, milestones, risks, blockers, and mitigation plans to product and engineering leadership.
Mentor and support engineers through technical guidance, onboarding, and knowledge sharing.
Collaborate with teammates to prioritize work, resolve complex problems, and deliver improvements within agreed timelines.

Requirements

~2 min read

The ideal candidate is an experienced fullstack engineer with strong architectural skills, practical ML systems knowledge, and the ability to take ownership in a fast-moving product environment. The following qualifications are expected:

At least 7 years of software engineering experience, preferably within SaaS environments.
Strong proficiency in modern JavaScript technologies, including React, Redux, Node.js, and Express.
Demonstrated experience operating ML-driven features in production, particularly vector similarity search using technologies such as pgvector, OpenSearch, or equivalent solutions.
Experience building feature pipelines that maintain consistency between model training and serving.
Knowledge of monitoring model drift, data quality, and production ML performance.
Proven ability to design multi-stage matching or recommendation systems, including candidate generation, filtering, and re-ranking.
Experience using real-world feedback, such as acceptances, approvals, and content performance, to improve recommendation quality.
Strong understanding of modern web application architecture, front-end architecture, component-driven development, and state management.
Extensive experience designing and maintaining scalable, high-throughput REST APIs.
Proven experience building and maintaining CI/CD pipelines and continuous delivery workflows.
Hands-on experience with AWS services such as RDS, S3, Lambda, and Elastic Beanstalk, as well as cloud-native architectures.
Solid knowledge of HTML5, CSS, and modern web application development patterns.
Ability to investigate incidents and identify performance bottlenecks across memory, I/O, and CPU resources.
Strong problem-solving, communication, prioritization, and time-management skills.
Ability to work independently, collaborate effectively, mentor teammates, and take ownership of technical initiatives.
A Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline is preferred.