Staff Machine Learning Engineer
J
JobgetherArtificial intelligence
Work remotely from eligible locations in Ontario, British Columbia, or Alberta, CanadaFull-TimeStaff
SalaryEstimated annual salary range of $160,320–$200,400, with a target annual bonus of 12.5%.
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Job Details
- Experience
- At least 8 years of applied AI/ML experience
- Required Skills
- AWSPythonGCPAzureMLOps
Requirements
- At least 8 years of applied AI/ML experience.
- Demonstrated experience owning technically ambitious systems end to end and making architectural and implementation decisions.
- Strong proficiency in Python and experience developing production-grade machine learning applications and services.
- Solid foundations in statistical machine learning, transformer architectures, and large language models, including their capabilities, limitations, and evaluation requirements.
- Extensive experience with MLOps and LLMOps patterns, including designing evaluation metrics for non-deterministic AI features at scale.
- Experience building cloud-based services on AWS, GCP, Azure, or comparable platforms.
- Experience with systems involving high-volume data, streaming pipelines, real-time inference, and diverse data stores.
- Ability to define solutions from limited initial requirements and move projects from concept to production in ambiguous technical environments.
- Familiarity with AI-assisted development tools such as Claude, Codex, GitHub Copilot, or Cursor.
- Ability to learn new technical frameworks and adapt to evolving AI capabilities while maintaining high engineering standards.
- Experience building evaluation frameworks for autonomous agents and LLMs is an asset.
- Experience developing autonomous agents that support multi-turn conversations and maintain long-term context and consistency is an asset.
Responsibilities
- Research, design, and implement LLM inference and orchestration techniques for scalable production use.
- Apply fine-tuning, distillation, and other optimization techniques to improve model performance and efficiency.
- Design, develop, and operate AI/ML systems and cloud services for high-volume data processing, streaming, and real-time inference.
- Develop evaluation frameworks, metrics, and tooling to assess AI/ML behavior, output quality, reliability, and performance.
- Design and implement operational foundations for deploying, monitoring, evaluating, and improving ML models and LLM applications.
- Contribute to agentic systems that support multi-turn interactions, contextual consistency, and reliable behavior across complex tasks.
- Identify and integrate AI development frameworks, tools, and techniques.
- Own technical initiatives from exploration and experimentation through implementation, production deployment, and optimization.
- Collaborate with engineering colleagues and stakeholders to align platform capabilities with product requirements.
- Share technical expertise through documentation, knowledge sharing, research, and open-source contributions.
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