Design, train, and deploy ML models for fraud detection, synthetic identity, and deepfake detection. Build evaluation pipelines, labeled datasets, benchmarks, and monitoring. Productionize models with backend engineering for latency, throughput, and reliability. Research adversarial approaches, multimodal fusion, and active-learning; partner with product and threat intelligence to convert fraud patterns into trainable signals.
About the Role
We're hiring a Machine Learning Engineer to join our core ML team in Toronto. You'll design, train, and deploy the models that power Tofu's fraud detection, deepfake analysis, and identity verification systems — directly shaping the accuracy and reliability of the product at scale.
What You'll Do
- Design, train, and ship ML models for fraud detection, synthetic identity classification, and deepfake (audio, image, video) detection.
- Build and maintain robust evaluation pipelines, including labeled datasets, benchmarks, and continuous monitoring for model drift.
- Productionize models in collaboration with backend engineers — owning latency, throughput, and reliability requirements end-to-end.
- Research and prototype novel approaches to adversarial fraud, including multi-modal signal fusion and active-learning loops.
- Partner with product and threat intelligence to translate emerging fraud patterns into trainable signals.
What You'll Bring
- A bias toward shipping — comfortable balancing research rigor with pragmatic delivery in a fast-paced environment.
- Strong analytical and problem-solving skills, with deep curiosity about adversarial systems.
- Excellent communication and the ability to explain trade-offs and model behavior to non-ML stakeholders.
- A collaborative mindset and willingness to mentor more junior engineers and researchers.
- Strong technical documentation skills.
Required Experience
- Bachelor's degree in Computer Science, Machine Learning, Statistics, or a related field (Master's or PhD a plus).
- 5+ years of professional experience building and deploying ML systems in production.
- Expert proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX).
- Hands-on experience with at least one of: computer vision, audio/speech models, NLP, or anomaly detection.
- Experience deploying models on cloud platforms (AWS, GCP, or Azure) using Docker and Kubernetes.
- Strong SQL and data wrangling skills.
- Experience with vector databases, embeddings, and large-scale retrieval (Elasticsearch, FAISS, pgvector).
Preferred Experience
- Experience with deepfake detection, biometrics, or generative model forensics.
- Experience with MLOps tooling (MLflow, Weights & Biases, Kubeflow, SageMaker).
- Experience in fraud, trust & safety, or security-adjacent domains.
- Familiarity with adversarial ML and red-teaming techniques.
Why Tofu and this Role
- You'll help build the trust layer for the internet — one of the defining problems of the AI era.
- You'll join a team obsessed with building a generational company.
- Early engineers have real ownership, real impact, and unlimited growth.
Benefits & Perks
- Competitive salary + meaningful equity.
- Comprehensive health benefits.
- 3 weeks of vacation.
- New laptop and gear to do your best work.
- Tofu swag your friends will want to steal.
Similar Jobs
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.
Top Skills:
AirflowAWSClaude CodeCopilotCursorFeature StoreGCPGitLightgbmMlflowModel Hosting PlatformNumpyPandasPrefectPythonPyTorchScikit-LearnSnowflakeSQLXgboost
Blockchain • Fintech • Mobile • Payments • Software • Financial Services
Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.
Top Skills:
AirflowAWSClaude CodeCopilotCursorGCPGitInternal Feature StoreLightgbmMlflowModel Hosting PlatformNumpyPandasPrefectPythonPyTorchScikit-LearnSnowflakeSQLXgboost
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.
Top Skills:
Cloud InfrastructureData LakehouseData WarehouseEmbeddingsFeature StoreJavaKafkaKotlinKubernetesLightgbmModel ServingMonitoringObservabilityPythonPyTorchSQLTensorFlowWorkflow OrchestrationXgboost
What you need to know about the Toronto Tech Scene
Although home to some of the biggest names in tech, including Google, Microsoft and Amazon, Toronto has established itself as one of the largest startup ecosystems in the world. And with over 2,000 startups — more than 30 percent of the country's total startups — Toronto continues to attract new businesses. Be it helping entrepreneurs manage their finances, simplifying business operations by automating payroll or assisting pharmaceutical companies in launching new drugs, the city's tech scene is just getting started.
