Senior Staff Machine Learning Engineer

Senior Staff Machine Learning Engineer Category: Software Engineering Location: Mountain View, California Job ID: 14412 Company Overview Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible. Job Overview Come join Intuit as a Senior Staff Machine Learning Engineer (MLE). Senior Staff MLEs deliver end‑to‑end AI solutions that span multiple domains and products, influencing the strategic direction of machine learning and AI across the company. You will identify cross‑cutting opportunities, set technical direction for complex systems, and deliver scalable, responsible AI‑driven experiences that unlock customer and business value at Intuit scale. In this role, you’ll be expected to define and evolve ML architecture, guide multiple teams, and drive execution excellence across the full ML lifecycle—from experimentation to production. You’ll partner closely with AI scientists, product engineers, and business leaders to solve high‑impact problems and pioneer new capabilities that advance Intuit’s AI‑native platform. Responsibilities Technical Craft Lead the architectural design of complex, cross‑cutting ML systems and data platforms that serve multiple Intuit products. Drive the adoption of AI‑native design principles, ensuring that systems are built for adaptability, observability, and secure customer data usage. Build and scale end‑to‑end ML solutions using cloud‑native and open‑source technologies (e.g., AWS, GCP, TensorFlow, PyTorch, Ray, Spark). Define engineering standards, model governance, and MLOps best practices across teams for training, deployment, monitoring, and continuous improvement. Evaluate and integrate transformative technologies such as foundation models, retrieval‑augmented generation (RAG), and LLM fine‑tuning pipelines to accelerate product innovation. Resolve deeply complex issues across domains, often requiring novel solutions or architectural evolution for long‑term scalability. Execution Excellence Deliver within large‑scale strategic initiatives, identifying systemic architectural gaps and leading their resolution across multiple teams. Challenge roadmaps to achieve measurable outcomes in weeks—not months, while balancing technical risk, business priorities, and product velocity. Establish clear execution boundaries and integration contracts across teams to accelerate delivery while maintaining quality. Proactively monitor model and system performance, ensuring continuous improvement of reliability, fairness, and customer impact. Champion experimentation at scale—defining hypotheses, success metrics, and iterative validation frameworks that balance speed with rigor. Customer‑Centric Outcomes Translate emerging customer behaviors and business trends into bold ML‑driven solutions that redefine customer experiences across Intuit’s ecosystem. Collaborate with Product and Design to frame and validate high‑risk, high‑impact hypotheses through MVPs and data‑driven experimentation. Lead initiatives that use customer signals, behavioral data, and competitive insights to identify unmet needs and shape Intuit’s AI roadmap. Drive the development and deployment of models that directly improve measurable customer outcomes—conversion, engagement, trust, and satisfaction. Balance rapid delivery with long‑term technical sustainability, ensuring quality and performance at scale. Accelerating Teams & the Organization Act as a force multiplier, raising the technical bar across multiple ML and engineering teams (typically influencing 10–35 engineers). Mentor and develop Staff and Senior MLEs, building a strong culture of learning, quality, and execution excellence. Identify and drive resolution for cross‑team bottlenecks—architectural, tooling, or communication‑related—that limit productivity or scalability. Build alignment across AI, product, and infrastructure teams to accelerate delivery of strategic initiatives. Provide architectural guidance, influence design reviews, and serve as a key connector between product, data, and platform teams. Champion diversity of thought and inclusive innovation within the ML and engineering community at Intuit. Compensation & Benefits Bay Area, California: $214,000 - $289,500 base salary. The position is eligible for a cash bonus, equity rewards, and benefits in accordance with Intuit’s plans and programs. Pay offered is based on factors such as job‑related knowledge, skills, experience, and work location. Intuit conducts regular comparisons across categories of ethnicity and gender to drive ongoing fair pay for employees. Qualifications BS, MS, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience. 8+ years of experience in software or ML engineering, with at least 3+ years at Staff level or equivalent leadership scope. Proven track record of architecting and delivering production‑scale ML systems that impact millions of users. Expert in ML lifecycle management, feature engineering, and large‑scale model deployment. Deep hands‑on experience with modern ML frameworks and distributed systems (TensorFlow, PyTorch, Spark, Ray, Kubernetes, MLflow, etc.). Experience leading cross‑functional initiatives spanning multiple product or platform teams. Strong background in software engineering fundamentals: algorithms, distributed systems, data pipelines, and performance optimization. Excellent communication and influence skills, capable of aligning technical direction with organizational strategy. Familiarity with LLMs, GenAI, and applied responsible AI practices is a strong plus. #J-18808-Ljbffr