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Job Title: Senior ML Engineer (GCP)

Job Location: Remote - Canada

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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 workplace.

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.

📌 Senior ML Engineer (GCP) (Canada)
🏢 VBeyond
📍 Canada