About Bandana

Bandana’s mission is to improve the livelihoods of hourly workers through transparency and trust, starting with finding better jobs. We believe that hard work should lead to success, and that this applies to all, not just the privileged few.

Bandana today has hit PMF with job seekers, with over a million monthly active users. While the platform today focuses purely on a best in class job search experience, we aim to start providing value to users beyond the search itself. That means improving candidate applications and making them easier, driving features to motivate job seekers, and providing them help with interviewing prep.

Job search is broken today. Application volume is absurdly high, as job seekers apply to hundreds of jobs in the hopes of landing even one interview while employers wade through thousands of low-intent candidates for every job. Long term, Bandana aims to drop the hundred-to-one matching process to one-to-one. That means providing the best in class experience to both employers and job seekers, so that neither dread the hiring process any longer.

The Role

Bandana has rapidly grown to over a million monthly active job seekers since October 2023, fields millions of jobs for thousands of employers and is investing heavily into building the best job search and career platform for blue collar workers. With our first step in machine learning, we will evolve our existing heuristic-based job recommendation system. You will be the first machine learning engineer on the team and will be pivotal not only in building out our first ML products, but in laying the foundation for many initiatives to come. Our ideal candidate thrives at the intersection of engineering and data science, and is excited to own a critical business function as we scale.

Responsibilities

• Design and build our user-job matching system end-to-end, from data pipelines to production models
• Develop and iterate on job ranking algorithms that surface the most relevant opportunities to users
• Establish foundational ML infrastructure: experiment tracking, model serving, and monitoring
• Deploy and maintain containerized ML services in cloud environments
• Partner with product and engineering to translate user needs into ML-powered features

You might be a good fit if you…

• Have 4+ years of experience building and deploying ML systems in production
• Have strong practical experience with recommendation systems, ranking, or matching problems
• Have an understanding of MLOps best practices
• Are comfortable owning the full ML lifecycle without handoffs to other ML engineers
• Thrive in high velocity, collaborative teams with ambiguous requirements and high opportunity for ownership
• Have worked in a startup environment as an early or sole ML hire

Strong candidates may also:

• Have experience with two-sided marketplaces or job/talent matching platforms
• Have built real-time serving infrastructure for low-latency predictions
• Have experience building and owning an ETL pipeline
• Have deployed ML systems on Google Cloud Platform (Vertex AI, BigQuery ML, or similar)
• Have implemented frameworks for ML model evaluation
• Have experience with LLMs or embedding models for semantic matching

What We Offer

• $160,000 - $200,000 based on experience + equity
• Medical/Dental/Vision insurance
• Wellness benefit
• Influence over technical direction at a company early enough for it to matter
• A team that treats infrastructure as a first-class concern, not an afterthought
• Lunch!