Machine Learning Engineer - Relevance & Learning Systems

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.

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 an Applied Scientist to own how we measure, understand, and improve the accuracy of our AI agent. This role sits at the intersection of applied ML, evaluation science, and product. You’ll define what “good” looks like for our agent, build the systems to measure it, and lead the science work to improve it, including fine-tuning the LLM judges that power our evaluation pipeline. You’ll partner with ML Engineering and AI Engineering. What you will do is bring scientific rigor to the most important question at Wizard: is our agent getting better, and how do we know? This is a foundational hire on our science team. Evaluation is the starting point, and the role is scoped to grow into broader applied science work as the surface area of the agent expands (recommendations, personalization, ranking, multimodal, conversational understanding). WHAT YOU’LL DO * Define and evolve accuracy metrics across the full shopping experience (retrieval, ranking, recommendations, outcomes) * Design and run experiments to measure improvements and regressions * Build and maintain evaluation datasets, benchmarks, and scoring frameworks * Improve the LLM judges that power our evaluation pipeline: prompting, calibration, and fine-tuning where it matters * Translate ambiguous product questions into clear, measurable hypotheses and analysis * Partner with ML Engineers to validate model changes and guide iteration * Identify failure modes and edge cases, and drive improvements through data * Make agent performance visible, trusted, and actionable across product and engineering FIRST 3 MONTHS * Go deep on the agent, the current eval pipeline, and the metrics we use today * Audit existing accuracy metrics and benchmarks; identify gaps, blind spots, and signals that aren’t trustworthy * Build relationships with ML, AI Engineering, and Product * Ship one quick win: a missing benchmark, an improved metric, or a fix to a misleading signal * Establish a baseline view of agent performance the team can rally around MONTHS 3 TO 6 * Own the evaluation framework: datasets, metrics, scoring, reporting, both offline and online * Drive measurable improvements to LLM judge quality (calibration, fine-tuning where appropriate) * Run experiments that influence at least one significant model or product change * Stand up automated evaluation the team trusts before and after every launch * Build dashboards and reporting that make agent performance legible to leadership BEYOND 6 MONTHS * Lead applied science work on the next frontier as the agent grows: multi-turn evaluation, multimodal, personalization, ranking quality, conversational understanding * Influence team-level strategy on what we measure, what we improve, and why * Mentor and help grow the science function as it expands WHAT SUCCESS LOOKS LIKE * Clear, trusted accuracy metrics are consistently used across product and engineering * A robust automated evaluation framework for both offline and live experiments * Model and product changes are consistently measured before and after launch * Demonstrable improvements in LLM judge quality and eval coverage * Science leadership that informs what we build, not just whether it works CAREER GROWTH * Depth track: become the org’s authority on AI evaluation: eval strategy, judge models, agent benchmarking * Breadth track: expand into other applied science problems (recommendations, personalization, ranking, multimodal, conversational understanding) as those areas come online * Leadership track: Senior / Staff Applied Scientist, with technical leadership across the science function * As the agent gets more capable, the science problems get richer IDEAL BACKGROUND * 5+ years in Applied ML, AI Research, or Applied Science (PhD or equivalent depth strongly preferred) * Hands-on experience evaluating modern AI/ML systems: LLMs, agents, ranking, or recommendations * Direct experience with LLM-based systems: judge models, RAG, prompt engineering, fine-tuning, RLHF, or similar * Strong experimentation foundations: A/B testing, causal inference, statistical rigor * Proven ability to operate in ambiguity: defining problems, not just solving pre-defined ones * Clear, structured communication that influences across ML, engineering, and product 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.