Machine Learning Engineer

Jagger • Full-time • Greater Toronto Area, Canada, CA • C$ 175k - C$ 225k / year • 15h ago

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