Join a small AI infrastructure team focused on improving the speed and cost efficiency of large language model inference. You will work across model serving, GPU kernels, and production infrastructure, with meaningful ownership from customer conversations through implementation.
Compensation
Base salary is $200,000 to $300,000 USD annually.
Responsibilities
Deliver day-one support for new open-source models, tuning them for latency and throughput
Optimize model-serving systems, including batching, KV cache, speculative decoding, and quantization
Write and tune kernels using CUDA, HIP, and Triton across a range of accelerator platforms
Design, deploy, and operate heterogeneous clusters spanning multiple hardware vendors
Run production inference systems, improving reliability, observability, and cost per token at scale
Research Engineer to make generative models dramatically faster, smaller and cheaper to run on-device and on server, working across the whole efficiency stack.
Locations (2)
Cupertino, California, United States
Seattle, Washington, United States
Responsibilities
Develop and deploy optimizations algorithms to speed up inference of production diffusion models (PTQ, QAT, kernel fusion, model distillation)
Design hardware-aware architectures that fit within compute and memory limits with minimal loss of quality
Own the process end-to-end from prototyping, evaluation to deployment into the Apple ecosystem
Requirements
M.S. or PhD in Computer Science or a related field such as Electrical Engineering, Robotics, Statistics, Applied Mathematics
Designs, builds, and operates secure, scalable AI systems focusing on Retrieval-Augmented Generation pipelines, agentic AI, and enterprise-grade service delivery across Azure and AWS.
Responsibilities
Architect and implement AI solutions
Integrate and operate AI infrastructure
Develop and manage data pipelines
Build agentic AI solutions
Model evaluation and optimization
Requirements
Bachelor’s degree in Computer Science, Engineering, Information Technology, Data Science
Nice to have: Microsoft Certified: Azure AI Engineer Associate
Nice to have: Microsoft Certified: Azure Data Scientist Associate
Nice to have: Microsoft Certified: Azure Solutions Architect Expert
Nice to have: Microsoft Certified: Azure Developer Associate
Researches, prototypes and proves new GenAI and agentic ideas that raise Equinix’s internal productivity and create capabilities for external products.
Locations (2)
Toronto, Ontario, Canada
Redwood City, California, United States
Compensation
The targeted pay range for this position in the following location is / locations are:
Canada - Toronto Office TRO : 142,000 - 212,000 CAD / Annual
United States - Redwood City Office GHQ : 190,000 - 286,000 USD / Annual
Responsibilities
Identify and test GenAI and agentic ideas for internal productivity and external product use cases
Build prototypes quickly, validate them with business teams against defined success metrics, and hand off production-ready designs and reference implementations
Track new models, techniques and tools; document which apply to Equinix and which do not
Benchmark open-weight and commercial models on shared task datasets: quality, latency, cost, license terms
Fine-tune, distill and quantize models for defined tasks, including tool-use and agentic behavior, and show measured gains over the baseline
Join the IVI Intelligence Lab and help build AI features that ship in Ford vehicles. You will work across the stack, from cloud training and serving to on-device inference, in computer vision, speech, personalization, and language models.
Compensation
The expected annual salary range for this Ford of Canada position is $96,000.00 - $146,000.00 and individuals may be eligible to participate in our Annual Compensation bonus program.
Responsibilities
Ship on-device AI: Deploy and optimize models on the vehicle SoC using quantization, pruning, and distillation, meeting strict latency, memory, power, and thermal budgets across CPU, GPU, and NPU acceleration.
Integrate with the platform: Embed inference in the Android Automotive OS software stack through system services and native (C++ and JNI) code, working against camera, audio, and vehicle signal interfaces.
Build the data flywheel: Construct cloud training pipelines and MLOps practices that turn vehicle data, under strict privacy controls, into continuously improving models: ingestion, curation, training, evaluation, registries, and drift monitoring.
Personalize the vehicle: Develop vision and speech models that recognize who is in the car, so the vehicle can prepare their profile, settings, and preferred one-tap actions, as convenience features on a secure-by-design data path with automatic fallback to the device credential (PIN, pattern, or password).
Predict and adapt: Build context engines and predictive models that learn driver behaviour, curate useful next actions before they are requested, and adapt interface surfaces and drive coaching to what perception detects inside and around the vehicle.
GPU Compute & MLIR Compiler Principal Engineer — AI Workloads
Drive technical direction and execution of AI and Generative AI workloads on GPU platforms.
Responsibilities
Lead exploration and optimization of emerging workloads including LLMs, transformer-based models, computer vision, and other AI/ML applications.
Collaborate across architecture, compiler technologies and frameworks such as TVM, MLIR, and IREE, software, performance, and product teams to drive solutions from early exploration through product-quality implementation.
Provide technical leadership, conduct design reviews, and mentor engineers working across GPU AI initiatives.
Develop and optimize GPU solutions leveraging capabilities such as matrix acceleration, mixed-precision computation, quantization, and other hardware acceleration features.
Analyze end-to-end AI workload performance and identify bottlenecks across models, frameworks, compilers, runtimes, GPU kernels, and hardware architecture.
Requirements
Bachelor's degree in Engineering, Information Systems, Computer Science, or related field
Master's degree in Engineering, Information Systems, Computer Science, or related field
PhD in Engineering, Information Systems, Computer Science, or related field
Design, evaluate, optimize, and operationalize enterprise AI systems across the complete model lifecycle.
Compensation
Medtronic offers a competitive Salary and flexible Benefits Package
Responsibilities
Design and implement training, fine-tuning, and adaptation pipelines for open-weight Large Language Models using supervised fine-tuning, LoRA, QLoRA, and Parameter-Efficient Fine-Tuning (PEFT) techniques.
Build and curate high-quality datasets from operational data sources, incidents, runbooks, logs, knowledge articles, and validated interactions for model training and evaluation.
Develop model evaluation frameworks covering reasoning, instruction following, hallucination detection, tool calling, safety, robustness, and structured output quality.
Evaluate and optimize retrieval-augmented generation systems through retrieval analysis, embedding model comparison, reranking, chunking strategies, and groundedness assessment.
Design and execute agent and tool-calling benchmarks that measure task completion, tool selection accuracy, failure recovery, and operational safety.
Requirements
Bachelor's Degree in Engineering
MCA
MSc
Nice to have: direct preference optimization (DPO)
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