Role Description Within the Modular Platform Engineering group, we seek to understand and apply the capabilities that drive software delivery performance at Modular. We are heavily data driven and make use of the dora metrics to track progress as we run experiments to determine the impact of our team’s initiatives and investments. We are looking for candidates based on both their breadth and depth of experience from any number of specialties such as: - Software engineering - IAAS and PAAS - Test, build, compiler, kernel - Hardware - Machine learning We focus on compounding improvements of customer value delivery and exponential gains in velocity and quality. The goal is to bring together diverse, collaborative teams for intensive project work. We are a growing team who collaborate across disciplines on a day to day basis. A strong cross-collaborator is a must. Come and be part of our team and architect our software development life cycle at Modular. Join our world-leading AI infrastructure team and help drive our AI infrastructure stack forward. LOCATION: Candidates based in the US or Canada are welcome to apply. You can work out of one of our offices in Los Altos, CA, or remotely from home. What you will do: - Build monitoring, alerting and observability for Modular Software Development Life Cycle services. - Support internal engineers to help facilitate the future of ML software stacks. - Develop highly reliable and scalable services for developing, testing, and benchmarking across diverse OS and hardware targets. Qualifications - 4+ years of systems design experience such as distributed systems, high-performance concurrency, etc. - Experience building, deploying, and supporting cloud services (bonus points for multi-cloud). - Ability to create durable, reusable software frameworks that are leveraged across teams and functions. - Develops secure and high-quality production code, and reviews and debugs code written by others. - Experience with one or more cloud application programming languages (C, C++, Java, C#, Go etc.) and modern scripting languages (Bash, Python, etc). - Strong collaboration skills, ability to share information with internal and external engineering and product teams. - Experience, or deep interest, in machine learning technologies and use cases. - Creativity and curiosity for solving complex problems, a team-oriented attitude that enables you to work well with others, and alignment with our culture. - Experience using open source software components in production. - Devotion to quality and engineering excellence. - Strong written and verbal communication skills. - Strongly identifies with our core company cultural values. Requirements - Helpful but not required: LLVM, MLIR, or Python experience is a bonus. Benefits - Amazing Team: We are a progressive and agile team with some of the industry’s best engineering and product leaders. - Flexible Location: We want you to work where you are most productive and we are a remote-first company. We have offices in Palo Alto, CA and Seattle, WA for collaboration, and will open more next year. - World-class Benefits: In order to attract the best, we need to offer the best. Premier insurance plans, up to 5% 401k matching, very generous WFH setup stipends, flexible paid time off, and more are available to you! - Competitive Compensation: We offer very strong compensation packages, including stock options. We want people to be focused on their best work and believe in tailoring compensation plans to meet the needs of our workforce. - Team Building Events: We organize regular team onsites and local meetups in different cities. Working at Modular will enable you to grow quickly as you work alongside incredibly motivated and talented people who have high standards, possess a growth mindset, and a purpose to truly change the world.

Role Description Our Client is building an AI operations platform that enables enterprise organizations to automate complex end-to-end business processes through AI agents. Rather than simply assisting users, the platform executes real operational work across systems, workflows, and business functions. Starting with retail and consumer packaged goods (CPG) organizations, the company is helping enterprise teams eliminate manual processes, improve operational efficiency, and unlock measurable business outcomes. With strong traction and a rapidly growing customer base, Our Client is building a category-defining platform at the intersection of AI, automation, and enterprise operations. Key Responsibilities - Lead enterprise transformation initiatives for retail and CPG customers - Build executive-level business cases tied to measurable financial outcomes - Partner directly with COOs, CFOs, business unit leaders, and executive stakeholders - Define transformation roadmaps and prioritize high-impact operational opportunities - Drive adoption and organizational change across customer teams - Establish success metrics, baseline measurements, and ROI frameworks - Present transformation progress and business results to executive leadership and boards - Identify expansion opportunities and support enterprise-wide rollouts - Collaborate closely with product, engineering, and leadership teams - Act as the strategic bridge between technology capabilities and business value Qualifications - Experience within retail, FMCG, or consumer packaged goods organizations - Background in a top-tier consulting firm (MBB, Big 4, or equivalent) - Experience leading business transformation initiatives - Proven ability to engage and influence C-level stakeholders - Strong commercial and financial acumen - Experience developing business cases and outcome measurement frameworks - Track record of driving operational change and organizational adoption - Ability to work independently in a high-growth environment Preferred Qualifications - Experience leading multi-country or multi-business-unit transformation programs - Exposure to automation, workflow platforms, AI, or digital transformation initiatives - Experience with retail operations, category management, supply chain, or finance operations - Additional European language skills (French, Czech, Nordic languages) - Experience scaling pilots into enterprise-wide programs Benefits - Competitive compensation package (€120,000 - €250,000) - Meaningful equity participation - Unlimited AI tooling budget - High level of ownership and autonomy - Opportunity to help define a new category in enterprise AI - Direct collaboration with founders and executive leadership - Exposure to some of the most innovative retail and CPG organizations - Flexible remote working environment - Significant long-term growth opportunities

• Architect and evolve our cloud-native platform and services to support high-throughput, low-latency data processing patterns, customer-facing features, and design platform to meet scalability requirements. • Design scalable, distributed systems powering complex capabilities such as authentication & authorization, data lifecycle management, metadata management, operational intelligence, and real-time event processing. • Evolve the authorization service toward modern identity standards and customer-configurable, fine-grained access models that scale without a release for every new role including authorization for non-human identities (service-to-service, AI agents, MCP-based tooling). • Built systems that capture and enforce structured metadata at ingest and serve it through clean service contracts; understands where platform metadata plumbing ends and the semantic/ontology layer begins, and collaborates well across that boundary. • Build governance primitives for a regulated environment — compliance-grade audit trail, dataset-level access controls, and approval / eSignature workflows. • Collaborate with engineering and product teams to deliver infrastructure that supports new services, customer-facing applications, and high-volume data processing workloads. • Build and maintain infrastructure-as-code (e.g., CloudFormation, AWS CDK) to automate, standardize, and secure deployments to support online upgrades and on-demand infrastructure allocation. • Enhance observability and monitoring to ensure reliability, cost efficiency, and rapid incident response. • Champion best practices in distributed systems design, scalability, and performance optimization, and share architectural insights through design reviews and technical documentation.

• Build Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services. • Real-Time Inference at Scale: Build automation for deploying low-latency services to serve model inferences in milliseconds. You will power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults. • Feature Engineering & Data Strategy: You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains. • End-to-End MLOps: You will work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement considering developer experience. You will champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.