Staff Machine Learning Engineer
Remote
About Trellis
Trellis is a profitable, fast-growing, Series A start-up backed by leading VC’s like General Catalyst, QED, NYCA, and Amex Ventures.
Join our mission to simplify and automate shopping for home and auto insurance.
Our ML-powered insurance recommendations, and our GenAI-powered conversational interface, enable us to insure everyday Americans faster, cheaper, and smarter.
We’re fully remote. We believe it allows us to find the best talent wherever it is, provide our team with maximum flexibility, and stand out to candidates.
Trellis is led by third-time fintech entrepreneur, Daniel Demetri, who has previously founded multiple fintech and insurtech startups that now operate as publicly-traded companies.
Our Values
- Extreme Ownership – We fulfill our promises to each other and to our customers. We own our responsibilities 110%.
- Speed – We work quickly and pragmatically. We move fast and get things done.
- Craft – We are constantly learning and bettering ourselves and the way we work.
- Collaboration – We work transparently, solicit feedback, and leave our egos at the door.
- Positivity – We see a cup half-full, focus on the team's potential, and rise to challenges.
Position Overview
As a Staff Machine Learning Engineer on Trellis’s Real-Time Bidding team, you will build, deploy, and optimize the ML models that drive >$100 million of annual programmatic marketing spend. You will work side-by-side with our Data Engineering team to harness high-quality data, craft robust real-time solutions, and continuously enhance model performance in a low-latency, revenue-critical environment.
Who You Are
- Analytical & Detail-Oriented: You have a solid grounding in statistics and machine learning, with a keen eye for detail.
- Collaborative Communicator: You excel at working cross-functionally, ensuring technical and business trade-offs are clearly understood.
- Self-Motivated & Pragmatic: You thrive in fast-paced environments, managing multiple priorities while delivering practical, scalable solutions.
- Innovative Problem-Solver: You’re eager to tackle complex challenges, iterating quickly and learning continuously.
What You’ll Do
- Own the End-to-End ML Lifecycle:
- Design, build, deploy, and improve ML models that power our real-time bidding platform.
- Continuously monitor, evaluate, and optimize model performance for maximum ROI.
- Contribute to Business Strategy:
- Work cross-functionally with product and business stakeholders to translate high-level objectives into tangible, ML-driven solutions that maximize ROAS in programmatic auctions.
- Apply Statistical & ML Expertise:
- Utilize advanced statistical techniques and modern ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) to predict auction outcomes and user behaviors.
- Incorporate real-time feedback loops to adapt swiftly to shifts in the RTB marketplace.
- Drive Team Excellence:
- Mentor and guide team members through technical leadership, code reviews, and sharing best practices.
- Balance urgency with the delivery of robust, scalable solutions in a dynamic startup environment.
- Architect Scalable Services:
- Leverage Kubernetes and managed services on GCP to deploy and orchestrate low-latency, high-availability services.
- Implement best-practice observability, logging, and monitoring to ensure system reliability and efficiency.
What You’ll Need
- Advanced SQL & Data Handling:
- Proficiency with complex queries, performance tuning, and managing large-scale data processing.
- Experience collaborating with a Data Engineering team to ensure data integrity and efficiency.
- Real-Time Bidding / AdTech Knowledge:
- Experience with or good understanding of the RTB ecosystem (DSPs, SSPs, auctions, ROI optimization) and designing low-latency systems.
- Statistical & Machine Learning Fluency:
- Solid foundation in statistics, probability, and modern ML techniques.
- Proficiency with frameworks like TensorFlow, PyTorch, XGBoost/Catboost, or scikit-learn.
- Teamwork, Accountability & Communication:
- Demonstrated success working with cross-functional teams, clearly articulating technical and business trade-offs.
- Autonomy & Prioritization:
- Self-driven and capable of managing multiple priorities while making practical trade-offs in a dynamic startup environment.
- Cloud & Kubernetes Expertise:
- Proven experience designing, deploying, and maintaining services on GCP or another major cloud platform.
- Deep hands-on experience with Kubernetes for container orchestration and microservices architecture.
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
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture Engineering Excel FinTech GCP Generative AI Kubernetes Machine Learning Microservices ML models PyTorch Scikit-learn SQL Statistics TensorFlow XGBoost
Perks/benefits: Career development Competitive pay Equity / stock options Flex hours Flex vacation Gear Health care Startup environment Team events Unlimited paid time off
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