• Develop and maintain CI/CD pipelines for deploying ML and LLM models in production, ensuring rapid and reliable model iteration. • Optimize the deployment process for LLMs, considering cost, latency, and scalability constraints. • Automate model retraining and redeployment processes to respond to changing data patterns. • Set up and manage cloud and on-premise infrastructure for ML/LLM workloads on platforms such as AWS, GCP, or Azure. • Work with containerization and orchestration tools (e.g., Docker, Kubernetes) to ensure scalable and resilient model deployment. • Implement data versioning and validation processes to maintain data integrity throughout the ML lifecycle. • Use tools like MLflow for tracking experiments, managing model versions, and maintaining reproducibility. • Document MLOps/LLMOps practices and mentor team members on deployment best practices.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Join the Affirm team as a Senior Staff Machine Learning Engineer and become a pivotal part of our innovative ML team. Our team is dedicated to Affirm's mission of revolutionizing financial services with transparency and inclusivity at its core. We are utilizing advanced machine learning techniques ensuring responsible and accessible financial products. In this role, you will help shape the future of machine learning at Affirm. You’ll partner with ML Platform, engineering, product, and risk leaders to design, implement, and scale advanced modeling approaches that drive critical decisions across the company. You will elevate our modeling capabilities, influence architectural direction, and ensure our systems can support increasingly sophisticated workloads. You will mentor senior engineers, bring clarity to complex, ambiguous problems, and contribute to a cohesive long-term ML strategy. If you are passionate about modern machine learning and excited to drive high-impact innovation across a growing organization, Affirm is the place for you. What you’ll do - You will define and drive multi-year, multi-team technical strategy for machine learning across Affirm, ensuring alignment with company-wide priorities and influencing the roadmaps of partner teams and platforms. - You will lead the design, implementation, and scaling of advanced ML systems, setting the architectural direction for complex, cross-functional initiatives and ensuring systems remain reliable, extensible, and prepared for increasingly sophisticated modeling workloads. - You will partner deeply with ML Platform, product, engineering, and risk leadership to shape long-term modeling capabilities, define new opportunities for ML impact, and guide infrastructure evolution required for next-generation ML methods. - You will provide broad technical leadership across the ML organization, mentoring senior engineers, elevating design and code quality, and spreading ML expertise through documentation, talks, and cross-org guidance. - You will drive clarity and alignment on ambiguous, high-stakes technical decisions, resolving cross-team tensions, balancing competing priorities, and exercising judgment optimized for the broader engineering organization. - You will champion operational and system excellence at the area level, owning the long-term health, availability, and evolution of critical ML systems, and ensuring robust testing, monitoring, and reliability practices across teams. What we look for - You have 10+ years of experience researching, designing, deploying, and operating large-scale, real-time machine learning systems, with a proven record of driving technical innovation and delivering measurable business impact. Relevant PhD can count for up to 2 YOE. - You have experience leading end-to-end ML system design, from data architecture and feature pipelines to model training, evaluation, and production deployment. You use distributed frameworks such as Spark, Ray, or similar large-scale data processing systems. - You are proficient in Python and ML frameworks, including PyTorch and XGBoost. You are experienced with ML tooling for training orchestration, experimentation, and model monitoring, such as Kubeflow, MLflow, or equivalent internal platforms. - You have a strong understanding of representation learning and embedding-based modeling. You possess deep expertise in neural network-based sequence modeling, including architectures such as Transformers, recurrent, or attention-based models, and multi-task learning systems. You are comfortable designing and optimizing models that learn from sequential or temporal event data at scale. - You have deep hands-on experience with large-scale distributed ML infrastructure, including streaming or batch data ingestion, feature stores, feature engineering, training pipelines, model serving and inference infrastructure, monitoring, and automated retraining. - You provide strong technical leadership: defining long-term strategy, guiding research direction, and aligning work across teams. You are recognized as a trusted expert who can drive clarity and execution even in ambiguous problem spaces. - You demonstrate exceptional judgment, collaboration, and communication skills, enabling effective technical discussions with engineers, researchers, and executives. You mentor senior engineers, foster technical excellence, and contribute to a culture of continuous learning. - You have strong verbal and written communication skills that support effective collaboration across our global engineering organization. - This position requires equivalent practical experience or a Bachelor’s degree in a related field. Pay Grade - R Equity Grade - 9 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). CAN base pay range per year: $206,000 - $256,000 This posting is for an existing vacancy. #LI-Remote Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities. We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include: - Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents - Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses - Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge - ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. [For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records. By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.

• Lead a world-class organization of research scientists and machine learning experts focused on developing the next generation of optimization, prediction, targeting, and marketplace systems. • Define the scientific vision, research agenda, and technical strategy for large-scale advertising and marketing platforms. • Drive innovations that directly impact revenue, customer performance, and marketplace efficiency. • Translate cutting-edge research into production systems operating at internet scale while partnering closely with Product, Engineering, Data Science, and Business leadership. • Define and execute the long-term research strategy for optimization, targeting, bidding, forecasting, and marketplace systems. • Lead a global team of research scientists and applied researchers across multiple geographies. • Identify emerging opportunities in machine learning, artificial intelligence, optimization, and computational economics. • Foster a culture of scientific rigor, innovation, experimentation, and measurable business impact. • Drive development of large-scale machine learning systems for prediction, recommendation, targeting, and campaign optimization. • Oversee research from problem formulation through experimentation, deployment, and production monitoring. • Lead innovation across demand-side platform (DSP) optimization, campaign delivery, supply optimization, and audience targeting systems.

Role Description As a Machine Learning Operations Engineer II, you are a foundational builder who bridges the gap between complex machine learning development and robust core engineering infrastructure. You partner closely with data scientists, product engineers, and infrastructure teams to deploy, monitor, and scale machine learning solutions in production. You are deeply passionate about automation, system reliability, and scaling pipelines that turn raw code into highly available product features. You balance a technical engineering mindset with an operational focus to maximize the reliability of our production systems. Our team is passionate, empathetic, hard working, and above all else focused on improving the lives of our service professionals (our Pros). Our success is their success. In your day to day, you will: - Implement robust infrastructure for deploying, monitoring, and managing machine learning models in live production environments - Build automated, end-to-end machine learning pipelines focusing on feature engineering, model deployment, and continuous evaluation - Collaborate with data scientists and product engineering teams to operationalize complex models and elevate production readiness - Develop sustainable continuous integration and continuous deployment pipelines to support reproducible model release workflows - Establish comprehensive monitoring, logging, and alerting solutions to ensure peak model performance and system health - Support the continuous optimization of system architecture to improve scalability, uptime, and infrastructure cost efficiency - Evaluate emerging technologies and operational best practices to integrate meaningful upgrades into the team's engineering stack - Document architectural standards, technical processes, and operational procedures to promote cross-team knowledge sharing Qualifications - 3+ years of professional experience in MLOps, Data Engineering, or core infrastructure software roles - Demonstrated proficiency in backend programming languages with a strong emphasis on Python - Hands-on experience deploying, monitoring, and supporting machine learning models within distributed environments - Proven understanding of workflow orchestration systems (i.e. Apache Airflow) - Solid understanding of distributed batch or streaming data tools (i.e. Kafka, Spark) - Experience implementing automated continuous integration and continuous deployment software delivery workflows - Demonstrated ability to leverage AI tools to improve workflows, streamline execution, or enhance outputs - Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent work experience Requirements - Exceptional breadth of interest shown through tangible, self-initiated ventures or deep community involvement; you love trying new things and possess a demonstrated history of successfully pivoting or starting over in life and work - Strong communication and collaboration skills when working across cross-functional engineering pods - A proactive mindset dedicated to automation and eliminating manual operational engineering bottlenecks - High attention to detail when debugging shared infrastructure and pipeline exceptions Benefits - Remote environment: totally built to make you feel that we are all together in one space without leaving your home office! - Self Managed PTO: Beach? Mountains? Camping? Discovering new experiences? You are free to take time out as you need! - Flexible work hours: We believe that you can reach your professional and personal goals working with us and encourage you to have a work life balance! - A culture built on innovation that values big ideas: We are always open to new ideas that will improve the life of our Pros! - MacBook (or PC if you prefer!) + Setup Fee ($500): What is remote work without the right tools? Here at HCP, you can choose your computer and set up your home office!