• Construir la infraestructura de Machine Learning y MLops que permite a Data Scientists desarrollar, desplegar y mantener modelos de Machine Learning en producción siguiendo buenas prácticas industriales. • Responsable de crear herramientas, SDKs y pipelines automatizados que garantizan reproducibilidad, observabilidad y gobernanza de modelos, con enfoque en self-service y autonomía de equipos de ML. • Incluye productización y mejora constante de modelos complejos (bayesianos, ensembles) con estándares de calidad enterprise. • Construir CLI tools para deployment automatizado (ml-deploy, ml-rollback). • Implementar workflows automatizados MLflow (promotion dev → staging → prod). • Crear templates repositories con best practices (model template, API template). • Establecer buenas prácticas para productización de modelos bayesianos (sampling, inference, monitoring). • Documentar APIs y SDKs (Sphinx/MkDocs, tutoriales, examples). • Training sessions Data Scientists (certificación uso plataforma). • Desarrollar herramientas self-service para Data Scientists (80%+ autonomy target). • Implementar automated testing suites (model validation, performance checks). • Optimizar performance modelos bayesianos en producción (MCMC efficiency, inference speed). • Optimizar MLflow performance (caching, query optimization). • Construir monitoring dashboards para model health (accuracy, latency, convergence diagnostics). • Governance automation (approval gates, compliance checks). • Incident response & troubleshooting support. • Mantener y evolucionar SDKs/tooling basado en feedback DS. • Mejorar arquitectura serving modelos complejos (bayesianos, ensembles). • Documentación continua (runbooks, troubleshooting guides). • Office hours semanales Data Scientists (support + knowledge sharing).

• Build and optimize data pipelines and backend services to process device and behavioral data in real time. • Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production. • Turn raw data into production-ready features that feed our fraud detection systems. • Collaborate with platform and backend engineers to integrate models seamlessly. • Maintain high standards of security, privacy, and compliance. • Champion best practices in testing, documentation, and observability.

Ibotta is seeking a Principal Machine Learning Engineer to join our Core Data & Analytics team and contribute to our mission to Make Every Purchase Rewarding. We’re looking for someone who has a passion for building scalable machine learning solutions using technologies and tools such as Python, Spark, Databricks, AWS, Sagemaker, etc. As a Principal Machine Learning Engineer, you will act as a technical leader and delivery owner for complex, high-impact ML initiatives spanning foundation models, data systems, and large-scale ML platforms. This role will immediately define ML strategy and drive impact through the support, guidance, and collaboration with multiple teams as part of our analytics organization. You’ll advance your technical leadership skills by establishing best practices for the team and company, while mentoring other Machine Learning Engineers and Data Scientists on the team. This position is available in multiple locations: Denver, Colorado - if you are in or willing to relocate to the Denver area, this is a hybrid position requiring 3 days in office (Tuesday, Wednesday, and Thursday). Remote options are available for the following states - AZ, AR, CA, FL, GA, IL, IN, IA, KS, MD, MA, MI, MN, MO, MT, NV, NJ, NY, NC, OH, OK, PA, TN, TX, UT, VA, WA, WI. Not based in one of these areas? We will offer a relocation bonus to help make your move to the Mile High City a smooth one. What you will be doing: - Research, design, and deploy the next generation of Ibotta's ML architectures, workflows, and standards that seamlessly integrate with key product features across the organization. - Work with Ibotta architecture and Machine Learning Platform teams to ensure integration of machine learning services and pipelines in larger technology infrastructure. - Be the leading contributor, voice, and thought leader in a growing community of machine learning practitioners. - Identify, research, prototype and evaluate cutting edge big data and machine learning technologies. - Act as a liaison between technical teams and non-technical stakeholders to communicate complex concepts clearly. - Lead the design, development, and deployment of production-grade ML systems across the organization. - Communicate complex machine learning solutions, concepts and the results of analyses in a clear and effective manner to business stakeholders and technology leaders to maximize the effectiveness of machine learning initiatives. - Mentor ML Engineers and Data Scientists, fostering a culture of technical ownership, rigorous experimentation, and best practices. What we are looking for: - 10+ years of professional industry experience as a Machine Learning Engineer or Software Engineer, focused on deploying machine learning systems at scale. - Advanced knowledge of multiple ML frameworks like: Sklearn, TensorFlow, Sagemaker, Spark ML. - Expertise working with distributed big-data tools and event-based architectures, ideally Spark and Kafka. - Deep hands-on experience prototyping, building, releasing, and monitoring mission-critical machine learning models in high traffic applications. - Extensive experience working within a cloud-based infrastructure, ideally AWS. - Experience driving org-wide ML strategy or setting technical standards across teams. - Track record of mentoring senior engineers or leading cross-functional initiatives. - Comfort operating with ambiguity and influencing without direct authority. About Ibotta ("I bought a...") Ibotta (NYSE: IBTA) is a leading performance marketing platform allowing brands to deliver digital promotions to over 200 million consumers through a network of publishers called the Ibotta Performance Network (IPN). The IPN allows marketers to influence what people buy, and where and how often they shop – all while paying only when their campaigns directly result in a sale. American shoppers have earned over $2.6 billion through the IPN since 2012. The largest tech IPO in history to come out of Colorado, Ibotta is headquartered in Denver, and is continually listed as a top place to work by The Denver Post and Inc. Magazine. To learn more about what our Tech teams are doing day to day, visit Building Ibotta on Medium.com. Additional Details: - This position is located in Denver, CO and includes competitive pay, flexible time off, benefits package (including medical, dental, vision), Lifestyle Spending Account, Employee Stock Purchase Program, and 401k match. Denver office perks include paid parking, snacks, and occasional meals. - Base compensation range: $228,000 - $253,000 Equity is included in overall compensation package. This compensation range is specific to the United States labor market and may be adjusted based on actual experience. - Ibotta is an Equal Opportunity Employer. Ibotta’s employment decisions are made without regard of race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected status. - Applicants must be currently authorized to work in the United States on a full-time basis. - Applicants are accepted until the position is filled. - For the security of our employees and the business, all employees are responsible for the secure handling of data in accordance with our security policies, identifying and reporting phishing attempts, as well as reporting security incidents to the proper channels. Recruiting Agency Notice Ibotta does not accept agency resumes and is not responsible for any fees related to unsolicited resumes. Please do not forward resumes to any Ibotta employees. #LI-Remote #BI-Remote #BI-Hybrid

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may:  - Design original computational mathematics problems that simulate real mathematical research workflows; - Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis; - Base problems on real research challenges or practical applications from mathematical practice; - Verify solutions using Python with standard mathematical libraries; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for mathematicians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - Degree in Mathematics (Pure or Applied) or related fields; - Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent; - 2+ years of professional experience: applied, research, or teaching experience is applicable; - Experience with numerical methods and symbolic computation; - Ability to design problems that mirror real mathematical research workflows; - Familiarity with computational complexity theory; - Strong written English (C1+). How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $17 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.