The posting
About Tofu
How do you distinguish between real and fake in an AI world? This question has crippled the recruiting industry overnight. Tofu is building one of the most important companies of our time and the layer to fix it.
AI has made it easier than ever to fake credentials, impersonate real people, and apply to jobs at scale using synthetic identities. More than 60% of remote applicants are fake, there were 75M detected deepfakes last year, and the number of state-sponsored groups hired by Fortune 500s grew 220%.
The hiring funnel has become the front door to the enterprise, and AI has cracked it wide open. It’s a $96 billion problem that needs to be solved.
We’re changing that.
Tofu is building a new standard for security in hiring.
And we just raised $5M from Slow Ventures and Founder Collective to step on the gas.
If you want to help build the trust layer for the internet and redefine how identity works in the AI era, we’d love to talk.
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. Productize 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.
Who You Are
A bias toward shipping — comfortable balancing research rigour 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 behaviour 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.
Preferred Experience
Experience with deepfake detection, biometrics, or generative model forensics. Experience with vector databases, embeddings, and large-scale retrieval (Elasticsearch, FAISS, pgvector). 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 This Role is Exciting
You'll help build the trust layer for the internet — one of the defining problems of the AI era. You'll join a team on a mission to build a generational company. Early engineers have real ownership, real impact, and unlimited growth. You want your work to shape the future - you will be architecting the security layer for an exploding, underserved problem and get excited knowing thousands of companies will rely on your work. You want to have a massive impact and accelerate your career.
Benefits and Perks
Competitive salary + meaningful equity. Comprehensive health benefits. 3 weeks of vacation. Whatever tech and gear you want to do your best work. Fully comp'd meals when in the office. Tofu swag your friends will want to steal.
Work Location This role is based in either our Toronto or Montreal office.
