Forward Deployed Engineer - Delivery Prediction & AI Systems
at Narvar
Remote
🚀 The Role
As a Forward Deployed Engineer (FDE) at Narvar, you’ll combine your customer empathy and technical expertise to build predictive systems that power delivery confidence for millions of shoppers.
You’ll work directly with Narvar’s customers, diagnosing data issues, integrating APIs, and deploying AI-driven solutions that make delivery promises smarter, faster, and more reliable.
This is an ideal role if you’ve spent the last few years implementing or supporting complex products (ERP, analytics, SaaS platforms, etc.) and are now ready to build the systems yourself, using AI, automation, and production-grade engineering to deliver at scale.
🧩 What You’ll Do
- Collaborate with customer and internal teams to design, integrate, and deploy predictive models that improve delivery-date accuracy.
- Build and maintain data pipelines that power real-time insights and reporting.
- Use AI agents and automation to replace manual customer workflows and diagnostics.
- Own customer-facing delivery performance dashboards and reporting systems.
- Debug integrations, latency, and data quality issues across multiple environments.
- Operate as both engineer and consultant, translating real-world needs into durable, scalable systems.
⚙️ What You’ll Bring
- 5–8 years of experience in software engineering, implementation, or technical consulting.
- Strong programming fundamentals (Python preferred) and a curiosity to grow into AI/ML systems.
- Proven track record working directly with customers or external partners on technical deployments.
- Ability to debug and integrate APIs, ETL pipelines, or data mapping workflows.
- Familiarity with modern AI tools, LLM APIs, workflow automation, or prompt engineering.
- Excellent communication skills and empathy across business and engineering stakeholders.
- A bias for learning, iteration, and delivering measurable outcomes.
💡 Nice-to-Haves
- Prior experience in e-commerce, logistics, or time-series forecasting.
- Exposure to data observability, A/B testing, or model monitoring frameworks.
- Hands-on experience with AI/ML-powered automation.
- Certification or coursework in data science, machine learning, or analytics.
📈 Success in 6–12 Months
- Deploy delivery prediction for multiple customers with measurable accuracy gains.
- Use AI automation to eliminate manual reporting across internal and customer teams.
- Grow into a subject-matter expert on predictive delivery and operational AI.
- Influence product roadmap priorities based on customer feedback and live data performance.
Why Narvar?
We're on a mission to simplify the everyday lives of consumers. Post-purchase is a critical phase of the customer journey. That's why we created Narvar - a platform focused on driving customer loyalty through seamless post-purchase experiences that allow retailers to retain, engage, and delight customers. If you've ever bought something online, there's a good chance you've used our platform!
From the hottest new direct-to-consumer companies to retail’s most renowned brands, Narvar works with GameStop, Neiman Marcus, Sonos, Nike, and 1300+ other brands. With hubs in San Francisco, Atlanta, London, and Bangalore, we've served over 125 million consumers worldwide across 10+ billion interactions, 38 countries, and 55 languages.
Pioneering the post-purchase movement means navigating into the unknown. Our team thrives on this sense of adventure while nurturing a mindset of innovation. We're a home for big hearts and we leave our egos at the door. We work hard but we always make time to celebrate professional wins, baby showers, birthday parties, and everything in between.
We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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The range reflects the minimum and maximum target for new hire salaries for the position across the US. Within the range, individual compensation packages are based on factors unique to each candidate, including but not limited to, skill set, education and certifications, and work location.
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