Lead AI Engineer / Technical Lead Manager

Location

Toronto, Ontario

Employment Type

Full time

Location Type

Hybrid

Department

Technology and Innovation

Compensation

• CA$152,200 – CA$201,730 • Offers Equity • Offers Bonus

The posted salary represents the lowest and highest ranges RV Tech reasonably and in good faith expects to pay for the position. Any posted salary range pertains only to the estimated starting pay for the role. Actual starting pay is based on a number of factors, including, but not limited to, the candidate’s experience, skillset, qualifications, specific competencies, relevant education, and location.

About Us

Rivian and Volkswagen Group Technologies is a joint venture between two industry leaders with a clear vision for automotive’s next chapter. From operating systems to zonal controllers to cloud and connectivity solutions, we’re addressing the challenges of electric vehicles through technology that will set the standards for software-defined vehicles around the world.

The road to the future is uncharted. By combining our expertise across connectivity, AI, security and more, we’ll map a new way forward. Working together, we’ll create a future that’s more connected, more intelligent, more sustainable for everyone.

Rivian and Volkswagen Group Technologies Canada is proud to be a Great Place To Work® Certified company — 92% of employees at RV Tech Canada say it is a great place to work, compared to 60% at a typical company.

Role Summary

As a Lead AI Engineer / Technical Lead Manager, you will head the technical direction and execution of our LLM and Agentic Systems strategy, with a specialized focus on bridging cloud intelligence and embedded execution. You will lead a high-performing squad of engineers to develop, fine-tune, and deploy state-of-the-art models that drive core product innovation. You are a "player-coach" who balances deep-dive architectural design—including model optimization for edge hardware—with people leadership, ensuring your team delivers scalable, production-grade AI solutions.

Responsibilities

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Strategic Technical Leadership: Own the roadmap for LLM integration and Agentic workflow orchestration. You will move projects from conceptual research to high-availability production environments, ensuring performance parity across cloud and embedded edge devices.

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Team Management & Mentorship: Direct responsibility for the growth of a specialized AI/ML team. You will provide technical mentorship in both high-level Generative AI and low-level model optimization (quantization, pruning) to ensure your team can deploy to any target.

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Architecting Agentic Systems: Lead the design of complex, multi-agent autonomous systems. You will oversee frameworks that allow LLMs to automate workflows reliably in both resource-constrained embedded environments and high-compute cloud environments.

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System Integration & Scalability: Partner with Platform and Hardware teams to architect the "AI/ML Backbone." You will ensure infrastructure supports low-latency on-device inference, vector database scaling, and efficient data exchange across the hardware ecosystem.

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Defining "Production-Grade": Establish rigorous standards for MLOps and TinyMLOps, including CI/CD for ML, automated evaluation frameworks (RAG metrics, hardware-specific latency benchmarking), and model health monitoring.

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Cognitive Automation Strategy: Identify high-impact opportunities for cognitive automation (e.g., local summarization, real-time reasoning) and delegate execution to maximize accuracy within the power and memory envelopes of our hardware.

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Cross-Functional Diplomacy: Act as the primary liaison between AI Engineering and Product, Legal, and Hardware stakeholders. You will translate complex ML constraints—such as SRAM limitations or NPU capabilities—into clear business trade-offs.

Qualifications

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Advanced Degree: Master’s or Ph.D. in Computer Science, Machine Learning, or a related field.

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Proven Leadership: 7+ years of total experience in ML/Software Engineering, with 2+ years in a Lead or Management role managing small to mid-size technical teams.

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Generative AI & Optimization Expertise: Extensive experience with LLM fine-tuning (PEFT, LoRA) and a solid understanding of model compression techniques (INT8/FP16 quantization) for deployment.

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Technical Stack: Mastery of Python and PyTorch/TensorFlow. Strong proficiency in C++ or Rust is highly preferred for developing performance-critical inference engines on embedded targets.

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Production Lifecycle: Proven track record of taking ML models from notebook to 24/7 production environments, including experience with Edge AI runtimes (e.g., TensorRT, ONNX Runtime, or TFLite).

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Strategic Mindset: Ability to balance the "bleeding edge" of research with the pragmatic needs of stable, low-power, and maintainable embedded products.

Total Rewards

Total compensation packages for full-time positions include base salary, eligibility for an annual performance bonus, and eligibility