About the Company Our client is an early-stage, seed-funded startup building the trust layer for hiring in the AI era. AI has made it easier than ever to fake credentials, impersonate real people, and apply to jobs at scale using synthetic identities. This company is building a new standard for security in hiring — fraud detection, deepfake analysis, and identity verification for enterprise recruiting teams. They raised $5M from top-tier investors to step on the gas.

Location: Toronto OR Montreal, Hybrid 3x a Week

About the Role We're hiring a Machine Learning Engineer to join the core ML team. You'll design, train, and deploy the models that power fraud detection, deepfake analysis, and identity verification systems — directly shaping the accuracy and reliability of the product at scale.

Responsibilities 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

Qualifications Bachelor's degree in Computer Science, Machine Learning, Statistics, or related field (Master's or PhD a plus) 5+ years of professional experience building and deploying ML systems in production

Required Skills 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 is a HARD requirement

Preferred Skills Experience with deepfake detection, biometrics, or generative model forensics Experience with MLOps tooling (MLflow, Weights & Biases, Kubeflow, SageMaker) Experience with FAISS, pgvector, or similar retrieval systems Background in fraud, trust & safety, or security-adjacent domains Familiarity with adversarial ML and red-teaming techniques

What You'll Bring Bias toward shipping — comfortable balancing research rigor with pragmatic delivery Strong analytical and problem-solving skills with deep curiosity about adversarial systems Excellent communication and ability to explain trade-offs to non-ML stakeholders Collaborative mindset and willingness to mentor more junior engineers Strong technical documentation skills

Pay range and compensation package Competitive salary + meaningful equity Comprehensive health benefits 3 weeks vacation New laptop and gear

Equal Opportunity Statement We are committed to diversity and inclusivity in our hiring practices.