Monster Jobs lê cada vaga aberta e diz, de 0 a 100, o match dela com o seu currículo — de graça. Você está vendo esta página sem a parte mais importante dela: a sua.
Pode ser 94. Pode ser 31.
O ponteiro só crava quando lemos o seu currículo. Leva 1 minuto, de graça.
DOSSIÊ MJ-0996 · ACESSO PARCIAL
Senior Software Engineer, Machine Learning Platform
Airwallex · SG - Singapore
Esta é 1 de 74.389 vagas encontradas nos últimos 30 dias. Nós comparamos todas com o seu currículo — hoje, esta página nem sabe quem você é.
Grátis, sem cartão. Só o seu currículo.
Encontrada há 6 horas
Se candidatar no escuro custa uma noite.
Saber custa um minuto.
A conta que ninguém faz
Você, sozinho, numa noite
~20
vagas abertas e lidas uma a uma — a maioria descartada no terceiro parágrafo, quando o requisito que você não tem finalmente aparece. Dias assim, toda semana.
O Monster Jobs, enquanto isso
74.389
vagas já lidas e entendidas, prontas para ganhar nota contra o seu currículo no momento em que ele chegar. Você não procura mais vaga — recebe as que merecem a sua atenção.
Recrutador faz triagem de candidato.
Aqui, o recrutador é você — de vagas.
Cada uma chega com nota e motivo. Seu único trabalho é dizer sim ou não.
RELATÓRIO DE COMPATIBILIDADE · GERADO POR VAGA, POR PESSOA
Por que essa nota
Para quem tem conta, este bloco explica a nota em português — escrito sobre o seu histórico, não um texto genérico:
O raciocínio da nota é gerado para cada currículo depois do cadastro gratuito.
A seu favor
O que do seu histórico pesa a favor aqui.
O que falta
O que a vaga pede e seu currículo ainda não mostra.
1 minuto: você envia o currículo, nós escrevemos o resto.
Ninguém caça sozinho. Esta vaga foi encontrada por outra pessoa e entrou no acervo de todo mundo. A caça continua enquanto você lê isto — e a sua parte do acervo (74.389 vagas, comparadas com o seu currículo) só passa a existir quando você entra.
O que você decide sobre cada vaga
Cada vaga vira um caso com histórico: candidatei, entrevista, proposta. Estes botões são os reais — só falta a conta.
Descrição da vaga
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 2,300 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 to see it in full.
How you'll make impactYou’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 Singapore and work from the office five days a week.
What we're looking forEssentials
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’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.
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 RecruitersTo 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 email address. Please apply only through 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 opportunityAirwallex 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.
Leu tudo e ficou na dúvida? É exatamente isso que a nota resolve.
Por que criar conta agora
Leva 1 minuto
Envie o currículo e pronto. Sem formulário longo, sem questionário, sem teste.
Grátis de verdade
Sem cartão, sem período de teste que expira. Seu currículo só gera as suas notas.
Já tem vaga esperando
74.389 vagas encontradas nos últimos 30 dias — a sua nota nesta e em todas as outras sai assim que o currículo chega.