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Staff Backend Software Engineer

Remote

Trellis is a profitable, fast-growing Series A startup backed by top investors like General Catalyst, QED, NYCA, and Amex Ventures.

Our mission: make shopping for home and auto insurance faster, smarter, and easier for everyday Americans. Powered by ML and GenAI, we help people find better insurance — saving them time, money, and stress.

We’re fully remote, built for speed, and led by a third-time fintech founder with a track record of building public companies.

If you move fast, take ownership, and care about your craft, you’ll thrive here.

About the Role

Trellis is hiring a Staff Backend Engineer to advance our Real-Time Bidding (RTB) capabilities. You’ll join a nimble and driven team, playing a pivotal role in shaping the future of our product, architecture, and company.

This role uniquely blends product engineering with machine learning expertise. Your primary focus will be on leading, designing, building, and scaling the backend features of our RTB system – including service integration (FastAPI), database management and MLOps components of our system. This will be complemented by a focus on developing, deploying, and iterating on the machine learning models and features that power our bidding services.

You will be responsible for the integration of new automation products in the Real Time Bidding Services and the ML models they serve. You'll utilize Python, Kubernetes, and core GCP managed services (GKE, Pub/Sub, Dataflow, BigQuery, Vertex AI, etc.). If you excel at solving complex scaling challenges (aiming for 10x growth!), have deep experience building reliable cloud-native systems and products, and have an interest in applied machine learning, join our fast-paced team and define the future of our technology.

This is a fully remote position, based in the US or Canada, and will report directly into the Head of RTB, Thomas Boquet. 

What You’ll Do

  • Collaborate closely with business stakeholders and other engineers to deliver impactful solutions.
  • Integrate services and product features with databases (Cloud SQL, Redis) and messaging queues (Pub/Sub).
  • Lead the development of our MLOps tools for ML models.
  • Build and optimize large-scale data processing pipelines for feature extraction and data transformation using BigQuery and Dataflow.
  • Train, evaluate, deploy, and monitor machine learning models on Vertex AI to improve bidding performance.
  • Stay fast and focused, balancing speed, performance, and scalability to deliver impact.

What You’ll Need

  • A minimum of 7 years of professional software engineering experience, demonstrating progressive growth and significant hands-on expertise in both backend systems development and machine learning engineering
  • Strong proficiency in Python. Experience with FastAPI is a plus.
  • Deep understanding and practical experience with Google Cloud Platform (GCP), including:
  • Exposure to the end-to-end machine learning lifecycle: data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring in production.
  • Experience with MLOps principles and practices.
  • Familiarity with containerization and orchestration (Docker, Kubernetes).
  • A “get it done” mindset with the ability to make pragmatic decisions balancing speed, performance, technical debt, and scalability.
  • Strong communication and collaboration skills.

Trellis is a fantastic place to work

Join a talented, passionate team:

  • Flat, collaborative, transparent culture; get in at the ground floor and be a true business partner
  • Opportunities for growth and development within your role and all areas of the organization
  • 75th-percentile (competitive!) compensation
  • 100% remote work environment
  • Quarterly, fun team bonding events

 

Trellis additionally offers competitive benefits:

  • Unlimited vacation time
  • 100% employer-paid Platinum-tier health insurance for employee, 65% for dependents
  • Flexible Spending Accounts (FSAs)
  • 401(k) retirement savings plan
  • Bonuses and equity opportunities
  • Budget for home office equipment

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