# Senior Software Engineer, Machine Learning Platform **Company:** [Airwallex](https://scaleengineer.com/companies/airwallex) Senior Software Engineer, Machine Learning Platform at Airwallex focuses on building and scaling the ML infrastructure that powers risk decisioning across the AI-native financial operating system. You'll design reliable data and model infrastructure, productionize machine learning workflows, and collaborate with ML engineers and risk specialists to develop systems that protect billions of dollars moving through the platform daily. This role requires 5+ years of software engineering experience with 3+ years in MLOps, model training infrastructure, or model serving systems, combined with deep expertise in modern deep learning frameworks, distributed orchestration tools, and GPU optimization. **Role:** ML Engineer **Seniority:** Senior **Locations:** US - Seattle **Salary:** 140–245 USD [Apply](https://jobs.ashbyhq.com/airwallex/32192a89-66ef-460d-9e91-f44ff97da094) Canonical: https://scaleengineer.com/jobs/airwallex/senior-software-engineer-machine-learning-platform-32192a89 --- ## Responsibilities - Design and Build ML Infrastructure: Architect reliable, scalable data and model infrastructure that supports the development, deployment, and monitoring of machine learning models across Airwallex's risk decisioning platform. Design systems capable of handling billions of events daily while maintaining low-latency inference capabilities. - Productionize Machine Learning Workflows: Transform experimental ML models into production-grade systems by implementing robust model training pipelines, serving infrastructure, and monitoring frameworks. Ensure models are reliably deployed and versioned across the platform. - Optimize Experimentation and Decisioning Speed: Improve the velocity and quality of ML experimentation and risk decisioning by reducing infrastructure bottlenecks, optimizing training execution, and enabling rapid model iteration cycles. Work on reducing latency for real-time fraud detection and payment authorization decisions. - Collaborate with Cross-Functional Teams: Partner closely with machine learning engineers, data scientists, product managers, and risk specialists to translate complex fraud and adversarial system problems into dependable, scalable technical solutions. Participate in design reviews and architectural decisions. - Implement Distributed Orchestration Systems: Build and maintain distributed workflow orchestration and model training execution systems using tools like Kubernetes, Ray, Kubeflow Pipelines, or Airflow. Ensure systems can scale horizontally to handle enterprise-grade ML workloads. - Optimize GPU and Hardware Acceleration: Leverage GPU architecture and hardware acceleration techniques to optimize model training and inference performance. Profile performance bottlenecks using specialized tools and implement optimizations to reduce training time and inference latency. - Own Platform Roadmap and Reliability: Take ownership of the ML platform roadmap, making technical decisions independently and driving improvements to platform reliability, scalability, and developer experience. Identify and fix infrastructure issues proactively without waiting for direction. ## Requirements ### education - {"name":"Bachelor's Degree in Computer Science or Engineering","description":"Bachelor's degree in Computer Science, Software Engineering, or a related technical field required."} - {"name":"Master's Degree (Preferred)","description":"Master's degree in Computer Science, Machine Learning, or related field is preferred but not required. May demonstrate advanced knowledge in distributed systems or ML optimization."} ### technical - {"name":"Model Training Infrastructure and MLOps","description":"Minimum 3+ years of hands-on experience building or maintaining model training infrastructure, MLOps platforms, or model serving systems at scale. Deep understanding of ML development lifecycle from experimentation through production deployment."} - {"name":"Deep Learning Frameworks","description":"Hands-on expertise with modern deep learning frameworks including PyTorch, TensorFlow, JAX, or similar. Experience optimizing models within these frameworks for performance and deployment efficiency."} - {"name":"Distributed Systems and Orchestration","description":"Strong proficiency with distributed orchestration and workflow management tools such as Kubernetes, Ray, Kubeflow Pipelines, or Apache Airflow. Experience deploying and managing containerized ML workloads."} - {"name":"GPU Computing and Hardware Acceleration","description":"Solid understanding of GPU architecture, hardware acceleration, and GPU-based optimization for both training and inference. Experience with CUDA, GPU memory management, and performance profiling tools like NVIDIA Nsight Systems."} - {"name":"Programming Languages","description":"Strong proficiency in core programming languages including Python, Java, or C++. Python expertise is essential for ML infrastructure work; Java or C++ experience valuable for high-performance backend systems."} - {"name":"Production Systems Design","description":"Experience designing and implementing low-latency, high-throughput production systems capable of handling real-time decision-making with millisecond response times. Understanding of reliability, monitoring, and observability in production environments."} ### experience - {"name":"Software Engineering Foundation","description":"Minimum 5+ years of professional software engineering experience, demonstrating expertise in building reliable, scalable systems and strong software architecture principles."} - {"name":"ML Platform Development","description":"At least 3+ years of focused experience in model training infrastructure, model serving systems, or MLOps platform development. This should include hands-on work with production ML systems."} - {"name":"Large-Scale Systems Experience","description":"Demonstrated experience building or optimizing large-scale production systems, particularly those handling high-volume data pipelines or real-time decision-making. Experience with streaming data systems is highly valuable."} ## Skills ### required - {"name":"Python","description":"Expert-level proficiency in Python for ML infrastructure development, data pipeline construction, and system scripting. Essential for working with modern ML frameworks and tooling."} - {"name":"Kubernetes","description":"Strong hands-on experience deploying, managing, and troubleshooting containerized workloads on Kubernetes clusters. Experience with Helm, YAML configuration, and cluster architecture."} - {"name":"PyTorch or TensorFlow","description":"Deep proficiency with at least one modern deep learning framework, understanding both usage patterns and optimization techniques for production deployment."} - {"name":"Distributed Systems Design","description":"Strong understanding of distributed systems concepts including consistency, fault tolerance, scalability, and trade-offs in system design. Applied experience implementing distributed pipelines."} - {"name":"System Architecture and Design","description":"Ability to architect complex, scalable systems with attention to reliability, latency, throughput, and maintainability. Experience making trade-off decisions in technical design."} - {"name":"SQL and Data Querying","description":"Strong SQL expertise for data exploration, pipeline development, and analytics. Familiarity with modern data warehousing solutions."} ### preferred - {"name":"Large Language Models (LLMs) and Model Acceleration","description":"Experience working with LLMs, including fine-tuning, inference optimization, or serving at scale. Familiarity with model acceleration frameworks and quantization techniques."} - {"name":"NVIDIA CUDA and GPU Optimization Tools","description":"Experience with CUDA programming, NVIDIA Nsight Systems, or other GPU profiling tools. Understanding of GPU memory management and optimization for ML workloads."} - {"name":"Apache Airflow or Ray","description":"Hands-on experience with Apache Airflow for DAG-based workflow orchestration or Ray for distributed computing and ML training. Understanding of workflow orchestration patterns."} - {"name":"Cloud Platforms (AWS, GCP, Azure)","description":"Production experience building and scaling ML infrastructure on cloud platforms like AWS, GCP, or Azure. Familiarity with managed ML services and cost optimization."} - {"name":"Fintech or Fraud Detection Systems","description":"Background in financial services, fraud detection, or risk systems. Understanding of adversarial systems, real-time decision-making, or compliance-critical infrastructure."} - {"name":"Java or C++","description":"Proficiency in Java or C++ for building high-performance backend systems, particularly for serving and low-latency inference engines."} ## Tech stack ### tools - {"name":"Kubernetes","description":"Container orchestration platform for deploying and managing ML training and serving infrastructure at scale."} - {"name":"Kubeflow Pipelines","description":"Kubernetes-native workflow orchestration platform for ML pipeline management and execution."} - {"name":"Apache Airflow","description":"Distributed workflow orchestration tool for managing complex ML training and data processing DAGs."} - {"name":"Ray","description":"Distributed computing framework for scalable ML training and hyperparameter tuning."} - {"name":"NVIDIA Nsight Systems","description":"GPU profiling and performance analysis tool for identifying bottlenecks and optimizing training and inference."} ### others - {"name":"Streaming Pipelines","description":"Real-time data streaming infrastructure for processing billions of events daily with millisecond latency."} - {"name":"GPU Computing","description":"Hardware acceleration leveraging GPU architecture for model training and inference optimization."} - {"name":"Model Serving Infrastructure","description":"Production systems for deploying models and making low-latency predictions at scale."} - {"name":"LLM Agents","description":"Large Language Model-based autonomous agents for alert triage and decision automation."} ### databases - {"name":"Graph Databases","description":"Used for exposing fraud rings and detecting coordinated fraud patterns across transactions and entities."} - {"name":"Time-Series Databases","description":"For storing and querying high-volume event streams and time-series metrics from the risk decisioning pipeline."} ### languages - {"name":"Python","description":"Primary language for ML infrastructure, data pipelines, and system scripting in modern ML platforms."} - {"name":"Java","description":"Used for high-performance backend services, model serving infrastructure, and low-latency decision systems."} - {"name":"C++","description":"Employed for performance-critical components, hardware acceleration, and optimized inference engines."} ### frameworks - {"name":"PyTorch","description":"Modern deep learning framework for model development, training, and inference optimization."} - {"name":"TensorFlow","description":"Comprehensive ML framework supporting model development, training, and production deployment."} - {"name":"JAX","description":"High-performance numerical computing library for ML research and optimization-critical workloads."} ## Benefits ### benefits - {"name":"Global Impact at Scale","description":"Work on systems protecting 150,000+ businesses and over USD 260 billion in annual transactions across 200+ countries and 90+ currencies. Your infrastructure decisions directly impact global financial operations."} - {"name":"Cutting-Edge ML Infrastructure","description":"Build and optimize state-of-the-art machine learning infrastructure handling billions of daily events with millisecond decision latency. Work with modern frameworks, GPUs, and distributed systems at global scale."} - {"name":"Autonomy and Ownership","description":"Own your technical roadmap independently, make architectural decisions from first principles, and drive improvements without requiring approval. Operate with founder-level energy and entrepreneurial mindset."} - {"name":"Challenging Problem Domain","description":"Tackle adversarial systems problems in fraud detection and risk decisioning where the stakes are measured in dollars. Work on sophisticated challenges balancing fraud prevention with legitimate business experience."} - {"name":"Collaborative Global Team","description":"Partner with world-class machine learning engineers, data scientists, product managers, and risk specialists across Airwallex's 27 global offices. Benefit from cross-timezone collaboration and diverse perspectives."} - {"name":"Financial Infrastructure Credibility","description":"Work for a fintech leader with 85+ regulatory licenses operating across North America, Europe, Middle East, and Asia-Pacific. Contribute to the regulated backbone of global commerce."} - {"name":"Career Growth in AI and ML","description":"Develop deep expertise in production ML systems, distributed infrastructure, and emerging technologies like LLMs. Build a portfolio of large-scale ML platform work recognized across the industry."} ## Compensation - **max:** 260000 - **min:** 180000 - **currency:** USD - **stockOptions:** true ## Interview process ### steps - {"name":"Initial Screening","description":"Recruiter phone screen to discuss your background, experience with ML infrastructure, and alignment with Airwallex's mission and culture. Expect 20-30 minutes discussing your technical experience and interest in the role."} - {"name":"Technical Skills Assessment","description":"Deep-dive technical discussion covering your hands-on experience with ML frameworks, distributed systems, and GPU optimization. Likely includes questions about specific projects, architectural decisions, and technical problem-solving approach."} - {"name":"System Design Interview","description":"Design-focused interview where you'll architect an ML platform component or production system. Expect to discuss scalability, reliability, trade-offs, and implementation details. May involve designing a model serving system, training pipeline, or monitoring infrastructure."} - {"name":"Cross-Functional Panel","description":"Interviews with team members including ML engineers, data scientists, and engineering leadership. Assess collaboration capabilities, communication clarity, and ability to work across diverse technical disciplines."} - {"name":"Risk Domain Discussion","description":"Conversation with risk specialists or domain experts to understand the fraud detection and financial risk domain. No prior fintech experience required, but curiosity about adversarial systems and risk problems is important."} - {"name":"Executive Interview","description":"Final conversation with engineering leadership or hiring manager focusing on long-term vision, strategic thinking, and culture fit. Discuss your approach to ownership, ambiguity, and building technical teams."} ## Full description ## **About Airwallex** Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own. We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. We're co-headquartered in San Francisco and Singapore, with more than 2,700 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our[ operating principles](https://www.airwallex.com/en-us/operating-principles) to see it in full. ## How you'll make impact You’ll build and scale the machine learning platform that powers risk decisioning across Airwallex, helping teams develop, deploy, monitor, and improve models that protect every dollar moving through our platform. You’ll design reliable data and model infrastructure, productionise machine learning workflows, and improve the speed and quality of experimentation and decisioning across the Risk Platform. You’ll partner closely with machine learning engineers, data scientists, product managers, and risk specialists to turn complex fraud and risk problems into dependable systems. You’ll be based in Seattle and work from the office 3 days a week. ## What we're looking for ### Essentials * 5+ years of software engineering experience, with at least 3+ years focused on model training infrastructure, model serving systems, or MLOps platforms. * Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. * Hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and model training execution engines. * Strong proficiency in core programming languages such as Python, Java, or C++. * Experience with distributed orchestration and workflow management tools (e.g., Kubernetes, Ray, Kubeflow Pipelines, Airflow). * Solid understanding of GPUs, including GPU architecture, hardware acceleration, and GPU-based training or inference optimization. ### Preferred * Experience with model acceleration frameworks and Large Language Models (LLMs). * Proficiency in performance profiling and bottleneck identification using tools like NVIDIA Nsight Systems for training and inference optimization. * Experience with cloud platforms (e.g., AWS, GCP) and building large-scale, low-latency production machine learning infrastructure. ## You'll thrive here if * You’re comfortable owning the roadmap yourself. * You own the outcome and you don't wait for permission to fix what's broken. * You're comfortable with ambiguity. Give you a problem, not a prescription, and you'll run with it. * You enjoy working closely with people across multiple countries and time zones as part of one connected, global team, including flexing your hours occasionally to make that connection work. * You value in-person collaboration and are happy being in the office five days a week. ## Learn more about your team Risk Platform builds the decisioning infrastructure that sits between Airwallex and every dollar that moves through it, protecting 150,000+ businesses moving over US$260 billion a year across 200+ countries and 90+ currencies, and deciding, often in milliseconds, whether a new signup is real, a payment is safe, or a login is who they claim to be. The hard part is that fraud evolves fast, and every decision carries a two-sided cost: miss an attack and money is lost, over-block and a legitimate business can't get paid. We build this with streaming pipelines processing billions of events a day, graph databases exposing coordinated fraud rings, ML models scoring every transaction, and LLM agents that triage alerts. You don't need a fintech background, just an appetite for adversarial systems problems where the scoreboard is measured in dollars. If you want to help scale one of the world's fastest-growing financial platforms safely, this is the team. ## **Applicant Safety Policy: Fraud and Third-Party Recruiters** _To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @_[_airwallex.com_](http://airwallex.com) _email address. Please apply only through_ [_careers.airwallex.com_](http://careers.airwallex.com) _or our official LinkedIn page._ _Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary._ ## **Equal opportunity** Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.