We Love AI JobsMachine learning, AI, QA
Meta BlackJoin the dynamic team at Meta Black in Toronto, focusing on various AI and Machine Learning roles, from leadership positions to specialized platform engineering. This is a unique opportunity to contribute to cutting-edge AI solutions, work with a talented group of professionals, and make a significant impact in the field of AI and machine learning.
Skills & Expertise
Key Responsibilities
Lead the delivery of production-grade AI solutions across various roles.
Build and maintain the infrastructure for seamless ML productionalization.
Ensure the quality and reliability of complex AI/ML systems through validation.
Full Description
"We're Hiring! Multiple AI & Machine Learning Roles (Lead, Platform, Agentic, & QA)"
Location: Toronto, Canada.
1. Machine Learning Technical Lead
Role: Lead the delivery of production-grade AI solutions.
Key Requirements: 10+ years in ML/AI; mastery of Python, SQL, and PyTorch/TensorFlow. Expert in MLOps (MLflow, Airflow, Feature Stores) and deploying scalable REST/gRPC services.
Infrastructure: Deep experience with Kubernetes, Docker, and Cloud (Azure/AWS/GCP).
Preferred: Databricks/Spark experience, Responsible AI (explainability/fairness), and GenAI/RAG patterns.
Focus: Driving clarity in ambiguity and owning end-to-end model lifecycles.
2. Machine Learning Platform Engineer
Role: Build the backbone for seamless ML productionalization.
Key Requirements: Strong Software Engineering (Clean Architecture, API design) and Python/SQL skills. Hands-on experience with MLflow, experiment tracking, and model versioning.
Tech Stack: Scikit-learn, XGBoost, PyTorch; proficient in CI/CD and containerized deployments.
Preferred: Databricks (Unity Catalog/Workspaces), Spark for large-scale data, and model serving optimization (FastAPI, ONNX, quantization).
Focus: Balancing latency, cost, and reliability in ML services.
3. Agentic AI Developer
Role: Design autonomous AI agents and multi-step LLM workflows.
Key Requirements: Expertise in LangChain/LangGraph or LlamaIndex and vector databases (Pinecone/Chroma). Mastery of prompt engineering, tool/function calling, and RAG.
Software Skills: Production Python API design, SQL, and data engineering fundamentals.
Preferred: Serving open-source LLMs (vLLM, Ollama), advanced agent patterns (reflection/memory), and Databricks/Unity Catalog.
Focus: Building reliable, non-deterministic systems with robust evaluation harnesses.
4. QA/Validation Engineer (Agentic AI & ML)
Role: Ensure the quality and reliability of complex AI/ML systems.
Key Requirements: Experience in ML validation (leakage checks, metric selection) and GenAI testing (evaluating non-deterministic outputs). Strong Python and SQL automation skills.
Tools: MLflow, Docker, CI/CD, and data validation libraries (Pandas/NumPy).
Preferred: Great Expectations or Deequ for data quality; red-teaming LLMs; observability via OpenTelemetry.
Focus: Translating AI quality risks into automated, actionable engineering safeguards.
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