Senior ML Engineer (GCP)

JOB DESCRIPTION :- Job Title: Senior ML Engineer (GCP) Job Location: Remote - Canada Job Description: You will own the end-to-end ML model lifecycle from post-training through production — everything after the researchers hand off a trained model. This is not a research role. You are the engineer who takes models and makes them real: benchmarked, deployed, monitored, and integrated into live production applications. You will work directly with ML researchers, production engineers, and platform teams in a fast-moving hybrid cloud environment. Note: Need candidates with 10+ years of experience. GCP cloud experience is mandatory. looking for ML engineers, not GenAI/Agentic AI Engineers or MLOps Engineers. Technical Stack: 10+ experience Primary platform: Google Cloud Platform (inference, deployment automation, experimentation, sampling) Production integration: Java-based streaming pipelines (model integration layer) Infrastructure: Hybrid — on-premise streaming + GCP serving stacks Distributed systems: Working knowledge required for debugging and end-to-end testing (not deep expertise) Machine Learning frameworks: TensorFlow, PyTorch, JAX or similar Must-Have: Strong foundation in ML inference, deployment, and quality testing Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks — this is the single most important trait End-to-end problem-solving mindset — can own a problem from model handoff to user-facing behavior Core ML knowledge sufficient to benchmark models and collaborate with researchers Experience deploying models in cloud environments, ideally GCP. Good to Have: Exposure to Java or JVM-based systems (model integration happens in Java; deep expertise not required) Familiarity with streaming data architectures Experience in hybrid cloud/on-prem environments. What You Will Do: Inference & Deployment Evaluate and benchmark new ML inference frameworks to guide production decisions Deploy models to GCP and integrate them into production applications and Java-based streaming pipelines Own deployment automation end-to-end — from model handoff through live serving Monitor how models behave in production for real end-users. Performance & Quality Design and execute benchmarking, performance testing, and quality testing on ML models Perform model sampling to support quality evaluation and researcher feedback loops Debug issues across the full stack — from inference layer down to streaming pipelines. Cross-functional Collaboration Partner with ML researchers to provide benchmarking feedback and guide inference decisions — requires enough core ML knowledge to have a meaningful technical handshake Adapt rapidly to non-standard and evolving tech stacks across hybrid (on-prem + GCP) infrastructure. Education: Bachelor's or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.

Own the end-to-end ML model lifecycle from post-training to production, focusing on benchmarking, deployment, and monitoring. Integrate models into Java-based streaming pipelines and collaborate with researchers to optimize inference decisions.

Requires 10+ years of experience with mandatory expertise in Google Cloud Platform and ML inference. A degree in Computer Science, Engineering, or Mathematics is required, along with a proven ability to adapt to unfamiliar tech stacks.

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Google Cloud Platform
• ML Inference
• Model Deployment
• TensorFlow
• PyTorch
• JAX
• Java
• Streaming Pipelines
• Distributed Systems
• Model Benchmarking
• Performance Testing
• Quality Testing
• Hybrid Cloud
• Model Monitoring
• End-to-end Problem Solving
• Production Integration

• Technology
• Software
• Data & Analytics
• Engineering
• Science & Research

Formation minimale

Baccalauréat

Expérience minimale

10+ ans

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Niveau d’expérience

Mid-Senior level

Mode de candidature

La candidature directe est offerte