Senior Software Engineer, Gopuff

• 5+ years of software engineering experience, with meaningful work on optimization, logistics, marketplace dynamics, or applied ML systems
• Hands-on experience building AI agents, LLM-powered workflows, or autonomous decision-making systems (not just chatbots—systems that take action and drive measurable customer outcomes)
• Strong fundamentals in algorithms, data structures, and system design. Comfortable owning services end-to-end, from database schema to deployment pipeline
• Proficiency in Python, Go, or TypeScript, with experience in ML frameworks (PyTorch, scikit-learn) and data tools (SQL, Spark, dbt)
• Experience with real-time systems, event-driven architectures, or streaming platforms (Kafka, Flink, Kinesis)
• A customer-obsessed mindset. You naturally think about how your technical decisions affect the end user and make trade-offs that protect the customer experience
• A bias toward shipping. You prototype quickly, validate with data, and iterate relentlessly. You are energized by ambiguity and thrive when the solution space is wide open

Desirable

• We are looking for a Senior Software Engineer who will own and advance the systems that drive delivery efficiency at Gopuff. You will sit at the intersection of operations research, applied AI, and full-stack engineering—building intelligent systems that decide how drivers are paid, how orders are routed, how delivery windows are compressed, how drivers stay engaged through gamification, and how we retain the best drivers on the platform
• This is not a role where you simply maintain existing services. You will design and deploy AI agents that autonomously reason about complex logistics decisions: dynamically adjusting driver incentives based on real-time demand, predicting delivery times with high fidelity, designing gamification loops that keep drivers motivated, and surfacing actionable recommendations that reduce cost-to-deliver while keeping drivers fairly compensated and customers delighted
• Design, build, and deploy autonomous AI agents that make real-time decisions about driver pay, order batching, route sequencing, and delivery time estimation—always optimizing for the fastest, most reliable customer experience
• Develop agent architectures that combine LLM reasoning with structured optimization (linear programming, simulation) to solve multi-objective logistics problems
• Create feedback loops where agents learn from delivery outcomes, driver behavior, and customer satisfaction signals to continuously improve performance
• Build customer-facing intelligence: AI-powered ETA models that set accurate expectations, proactive delay notifications, and smart order prioritization that protects the customer promise
• Engineer dynamic pay systems that balance driver earnings, delivery speed, and unit economics—ensuring Gopuff is the most attractive platform for drivers while hitting margin targets
• Build experimentation frameworks to A/B test pay structures, bonus triggers, and incentive timing across markets and demand scenarios
• Develop models that predict driver supply elasticity and optimize incentive spend across peak, off-peak, and surge conditions
• Architect gamification engines that drive desired behaviors: achievement systems, streaks, leaderboards, tier-based rewards, and milestone bonuses that keep drivers engaged and performing at their best
• Build AI-powered personalization that tailors challenges and incentives to individual driver patterns—the right nudge, at the right time, for the right driver
• Develop real-time progress dashboards and in-app experiences that give drivers visibility into their earnings trajectory, performance rank, and next reward milestone
• Instrument and analyze gamification effectiveness: measure impact on shift coverage, delivery speed, customer ratings, and driver satisfaction using rigorous experimentation
• Develop predictive churn models that identify at-risk drivers before they leave, and trigger automated retention interventions (targeted bonuses, schedule flexibility, personalized outreach)
• Build onboarding optimization systems that reduce time-to-first-delivery and accelerate new drivers to peak performance through guided AI coaching
• Design loyalty and tenure reward programs that increase long-term driver commitment while improving delivery quality for customers
• Analyze the full driver lifecycle—acquisition, activation, engagement, retention—and build data products that give Operations teams actionable intelligence on driver health across every market
• Own the delivery time prediction pipeline: build and refine ML models that estimate prep time, transit time, and total delivery duration with high accuracy—because an accurate ETA is one of the strongest drivers of customer trust
• Architect systems for intelligent order dispatching—matching orders to drivers based on proximity, capacity, route efficiency, and predicted completion time
• Identify and eliminate bottlenecks across the fulfillment-to-doorstep chain using data-driven analysis and automated anomaly detection agents
• Write production-grade code in Python, Go, or TypeScript. Design APIs, data pipelines, and microservices that handle thousands of delivery decisions per minute
• Collaborate with Data Science, Operations, and Product teams to translate business goals into technical solutions with clear success metrics tied to customer outcomes
• Champion engineering excellence: code reviews, testing, observability, and documentation that keep the team moving fast without breaking things