Senior Technical Program Manager to drive strategy, planning, governance, risk management, and execution across complex Edge AI and systems software programs.
Responsibilities
Translate business and technology strategy into integrated program plans, roadmaps, milestones, and measurable outcomes.
Drive complex Edge AI and systems software programs from strategy and architecture through development, validation, and delivery.
Coordinate execution across architecture, software engineering, product management, quality, security, and business teams.
Work with architects and engineering leaders to align delivery plans with the target architecture and technical roadmap.
Establish clear deliverables, ownership, dependencies, readiness criteria, and success measures.
Requirements
Four-year or graduate degree in Computer Science, Engineering, Information Systems, Business Management, or a related discipline, or equivalent experience.
Lead design and implementation of critical AI product initiatives on Edge devices and develop both tactical and strategic AI solutions for Samsara’s physical operations customers.
Compensation
$170,400—$234,300 CAD
Responsibilities
Lead design and implementation of critical AI product initiatives on Edge devices
Develop both tactical AI solutions as well as more strategic and longer term research
Work with petabyte-scale data from customer operations including text, transactions, diagnostics, sensor, camera, and location data
Partner across business units to explore and prototype new AI experiences and optimize the ML model performance on edge devices
Stay connected to industry and academic research and adopt novel technology that suits Samsara’s needs
Design, build, and own the infrastructure and workflows behind machine learning and LLM-powered systems, ensuring reliable deployment from experimentation to production.
Responsibilities
Design, deploy, and operate MLOps platform infrastructure, cloud services (AWS, Azure, GCP), and LLM inference pipelines (Ollama, vLLM).
Build automated CI/CD pipelines, IaC, and monitoring/alerting systems to support RAG applications, AI agents, and production deployments.
Manage Linux servers, networking, security endpoints, and troubleshoot production issues alongside AI engineering teams.
Establish department-wide MLOps/DevOps standards, best practices, and reusable infrastructure templates.
Partner with AI Developers and RAG Engineers to guide architecture, deployment strategies, tool selection, and production readiness.
Lead the deployment, optimization, and scaling of AI workloads on embedded and edge computing platforms for autonomous mobile robots, drones, and industrial automation.
Responsibilities
Design, develop, and deploy AI/ML solutions on embedded and edge computing platforms
Optimize inference performance across CPU, GPU, NPU, DSP, FPGA, and heterogeneous SoC architectures
Build scalable Edge AI software pipelines supporting computer vision, sensor fusion, multimodal AI, and real-time decision-making applications
Deploy and optimize models using frameworks such as TensorRT, ONNX Runtime, OpenVINO, TFLite, TVM
Drive model optimization through quantization, pruning, compression, distillation, and hardware acceleration techniques
Requirements
Master's degree or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, Robotics, Computer Engineering, or a related discipline
Internship focused on developing on-device long‑term memory for automotive agents using multimodal small language models.
Responsibilities
Design and implement multimodal long‑term memory (RAG) for automotive agents in resource‑constrained in‑vehicle environments.
Fine‑tune LFM3 models, especially LFM3‑Omni‑2B, and other small language models for memory, preference extraction, and function calling (SFT, LoRA/QLoRA).
Apply reinforcement learning (e.g., DPO, GRPO) to improve memory, activation, and tool‑use decisions.
Build trigger‑based activation from multimodal cabin and vehicle signals, using decision models (e.g., LFM3 d1‑omni‑2B) and lightweight classifiers.
Build classifiers for turn detection, PII detection, and intent routing.
Internship role contributing to core pillars of HP IQ's AI Machine Learning platform, focusing on model inference optimization, orchestration, and fine-tuning.
Compensation
Hourly Pay Range: $40.00–$50.00 per hour. Actual compensation will be determined based on factors such as skills, qualifications, and experience.
Responsibilities
Work on model hosting and inference optimization, learning how to profile, benchmark, and accelerate model execution on resource-constrained devices; experiment with quantization, distillation, or other optimization techniques to reduce latency and memory footprint.
Contribute to orchestration frameworks and agent workflows, building or extending systems that coordinate multiple models and agents using tools like LangGraph or LangChain; develop an understanding of when agentic patterns are appropriate and how to design robust, scalable orchestrations.
Participate in model customization and fine-tuning, taking pre-trained language models and adapting them for specific use cases; conduct experiments, evaluate results, and iterate on model configurations to improve performance for target tasks.
Collaborate cross-functionally with system software, design, and product teams to understand how your work integrates into the broader platform; communicate progress, challenges, and technical decisions clearly and seek mentorship from engineers with deep expertise in your focus area.
Contribute to open-source projects or internal libraries relevant to inference, orchestration, or model training; demonstrate ownership of end-to-end outcomes and ownership of improving the platform's capabilities and reliability.
Internship role contributing to AI model inference optimization, orchestration, and fine‑tuning for HP IQ’s agentic devices.
Compensation
Hourly Pay Range: $40.00–$50.00 per hour. Actual compensation will be determined based on factors such as skills, qualifications, and experience.
Responsibilities
Work on model hosting and inference optimization, learning how to profile, benchmark, and accelerate model execution on resource‑constrained devices
Contribute to orchestration frameworks and agent workflows, building or extending systems that coordinate multiple models and agents using tools like LangGraph or LangChain
Participate in model customization and fine‑tuning, taking pre‑trained language models and adapting them for specific use cases
Collaborate cross‑functionally with system software, design, and product teams to understand how your work integrates into the broader platform
Contribute to open‑source projects or internal libraries relevant to inference, orchestration, or model training
Requirements
Bachelor's in Computer Science, Engineering, or related field
Nice to have: Open‑source contributions to inference engines (e.g., vLLM)
Nice to have: experience with vLLM
Nice to have: experience with system‑level profiling
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