AI Applied Scientist

Wizard

ABOUT WIZARD Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust. THE ROLE We’re looking for a Machine Learning Engineer to design and build feedback driven learning systems that improve our AI agent over time. This is not a traditional RL research role, we’re focused on building systems that learn from real user behavior and improve production. You’ll be working at the intersection of a live conversational agent and real shopping behavior – the feedback signal quality here is unusually rich compared to traditional search. You’ll focus on turning user interactions into learning signals, designing practical feedback loops and shipping systems that continuously improve real world outcomes. WHAT YOU’LL DO * Build and productionize feedback loops that improve agent performance over time * Build the evaluation infrastructure – offline metrics, regression suites, and experiment analysis * Own the signal pipelines end-to-end: instrument events, build clean labeled datasets, and translate user behaviors into reliable learning signals * Design lightweight reinforcement learning / bandit-style approaches where appropriate * Partner closely with product and engineering to define success metrics and optimize for them * Design and analyze experiments that validate whether learning system changes actually improve real outcomes * Improve ranking, recommendations and decision making within the agent * Iterate quickly: Ship → measure → learn → improve  What Success Looks like * You ship quickly and drive measurable improvements in core product metrics * You turn noisy user behavior into reliable learning signals that improve the agent over time * You own systems end to end and operate comfortably in production IDEAL BACKGROUND * 5-8 years hands on experience building and shipping ML systems * Bachelor’s or Master's degree in computer science * Experience shipping ML systems to production and have worked on recommendation systems, ranking, personalization or optimization problems * Deep knowledge in Python and model ML tooling * Pragmatic: you choose simple, effective solutions over theoretically perfect ones COMPENSATION & BENEFITS The expected base salary range for this role is $225,000 - $280,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities. In addition to base salary, Wizard offers: * Equity in the form of stock options * Medical, dental, and vision coverage * 401(k) plan * Flexible PTO and company holidays * Fully remote work within the United States * Periodic company offsites and team gatherings Wizard is committed to fair, transparent, and competitive compensation practices.

Wizard

ABOUT WIZARD AI At Wizard AI, we’re building the top-performing AI Shopping Agent that delivers the best products from across the web with unmatched accuracy, quality, and trust. Our ML models power the core of our platform, and we’re looking for a Senior Machine Learning Engineer to own how they run in production reliably, efficiently, and at scale. THE ROLE As a Senior ML Engineer on our Inference Platform, you’ll own the end-to-end lifecycle of production ML serving systems from model packaging and deployment to monitoring, optimization, and scaling. This is not a traditional MLOps role focused solely on pipelines and tooling. You’ll be responsible for the inference infrastructure powering a live conversational shopping agent, operating multiple specialized serving engines under real-world production load. You’ll own critical decisions around serving architecture, performance, reliability, and scalability, working closely with ML Engineers, Data teams, Product, and DevOps to ensure models move seamlessly from experimentation into high-performance production systems. WHAT YOU'LL DO * Own and evolve our multi-engine inference platform, supporting a variety of model types and serving requirements. * Build and improve production ML pipelines — taking models from experimentation to reliable, high-throughput serving. * Define and implement model versioning, rollout, rollback, and lifecycle management strategies that ensure reproducibility and operational reliability. * Define and enforce serving-layer SLAs, including latency, availability, GPU utilization, Time-to-First-Token (TTFT), and Inter-Token Latency (ITL). * Build observability, monitoring, alerting, and operational tooling for production inference systems. * Apply software engineering best practices, including testing, CI/CD integration, and reproducibility across ML workflows. * Optimize inference performance through efficient resource utilization, hardware-aware serving strategies, and cost-conscious infrastructure design. * Ensure ML serving systems are secure, scalable, and operationally resilient. * Partner with ML, Data, Product, and DevOps teams to turn ideas into production systems, driving the technical decisions on serving and scale. WHAT WE'RE LOOKING FOR * Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience. * 5–8+ years of experience in Software Engineering, ML Engineering, Platform Engineering, or Infrastructure Engineering, with direct ownership of production ML serving systems. * Hands-on experience running an LLM serving engine (vLLM, TGI, TensorRT-LLM, or SGLang) in production under real load — not just managed or hosted endpoints. * Strong Python skills and software engineering fundamentals, combined with deep systems and infrastructure knowledge. * Experience with cloud platforms such as AWS, GCP, or Azure, and familiarity with ML lifecycle tooling, experimentation platforms, and model registries. * Strong grasp of inference performance — continuous batching, KV-cache and GPU-memory behavior, quantization, and CPU-versus-GPU bottlenecks — with the instinct to profile before tuning. * Experience serving heterogeneous workloads, including LLMs, embedding models, and extraction models, each with distinct latency, throughput, and scaling requirements. * Demonstrated ability to balance latency, throughput, reliability, and infrastructure cost while operating production-scale ML systems. * Experience in high-growth startup environments and comfort operating in fast-moving, evolving technical landscapes. WHAT SUCCESS LOOKS LIKE RELIABLE, SCALABLE INFERENCE SYSTEMS Production serving infrastructure operates with clear SLAs, strong observability, and minimal downtime. Latency, availability, throughput, and GPU utilization are actively measured and optimized as platform demands grow. END-TO-END OWNERSHIP You own the complete serving lifecycle — from deployment and release management through monitoring, optimization, and scaling — enabling ML engineers to ship quickly while maintaining reliability and reproducibility. TECHNICAL LEADERSHIP AND IMPACT You shape the future of Wizard's inference platform, driving key architectural decisions that improve performance, reduce infrastructure costs, and support the next generation of AI-powered shopping experiences.

Wizard

THE ROLE We’re looking for a Senior Software Engineer to build and scale the backend systems that power our AI agent. This role sits at the intersection of backend engineering, machine learning, and product, and is focused on turning AI capabilities into reliable, production-ready systems. You won’t be training models, but you will make them work in the real world. You’ll build APIs, services, and data systems that connect LLMs and ML models to user-facing experiences, ensuring performance, reliability, and scalability. WHAT YOU’LL DO * Design and build APIs and backend services that power AI-driven product experiences * Develop systems that integrate LLMs and ML models into production workflows * Build and maintain data pipelines supporting training, inference, and evaluation * Partner closely with ML Engineers, Data Scientists, and Product to ship end-to-end features * Improve system performance, reliability, and scalability across services * Contribute to experimentation and feedback loops that improve model and product performance * Debug complex production issues and drive root cause resolution * Raise the bar on code quality, system design, and engineering standards WHAT SUCCESS LOOKS LIKE * AI-powered features are reliably delivered through scalable, well-architected backend systems * ML and LLM capabilities are seamlessly integrated into product experiences with strong performance and uptime * Clear, maintainable APIs and services enable fast iteration across engineering and product teams * Systems are designed with strong observability, enabling rapid debugging and improvement * Engineering decisions consistently balance speed, quality, and long-term scalability IDEAL BACKGROUND * 5+ years of experience in software engineering, with strong backend focus * Strong proficiency in Python and experience building production-grade systems * Experience designing APIs and service-oriented architectures * Experience working with ML/AI systems in production environments (LLMs, ranking, recommendations, or similar) * Familiarity with databases (SQL and/or NoSQL) and data-intensive systems * Experience with cloud platforms (AWS, GCP, or Azure) and modern infrastructure * Exposure to containerization and orchestration (Docker, Kubernetes) * Ability to operate in ambiguity and take ownership of loosely defined problems * Strong product mindset with focus on real user outcomes * Clear communication and ability to collaborate across engineering, ML, and product COMPENSATION & BENEFITS The expected base salary range for this role is $200,000–$225,000 USD and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities. In addition to base salary, Wizard offers: * Equity in the form of stock options * Medical, dental, and vision coverage * 401(k) plan * Flexible PTO and company holidays * Fully remote work within the United States * Periodic company offsites and team gatherings Wizard is committed to fair, transparent, and competitive compensation practices.