Owner of end-to-end ML platform and MLOps patterns including data preparation, model management, experiment tracking, and model serving. Work includes building scalable graph data pipelines (Beam, Spark, Ray Data), optimizing distributed GPU training and costs, and operating on GCP (BigQuery, GCS) with infrastructure-as-code (Terraform). Requires 5+ years in ML infrastructure and hands-on experience with Python, PyTorch/TensorFlow, Ray, Kubernetes, and tools like MLflow or Wandb.

Role involves defining and developing evaluation metrics and data-driven evaluation products (on-road analysis, large-scale simulation, models, dashboards) to support planner and controller development. Requires 8+ years experience, MS/PhD or equivalent in related field, strong Python (Pandas, NumPy, SciPy, Matplotlib), familiarity with Git/Linux, and experience with simulation, planning/control, and metrics for autonomous systems; C++ and Bazel experience are beneficial.

Responsible for building scalable multi-tenant inference services and APIs, improving observability and capacity management, partnering with platform and GPU teams, providing technical leadership, and mentoring junior engineers. Requires 8+ years experience with large-scale distributed systems, SRE practices, 2+ years Go and Kubernetes experience, and familiarity with LLM serving tools (vLLM, Triton, TensorRT-LLM).

Hands-on technical leader responsible for end-to-end design, development, and operation of distributed inference hosting and data plane components for large language and multimodal models. Role emphasizes performance optimization (tensor/data parallelism, KV cache, routing), GPU/interconnect awareness (NVLink, XGMI, RoCE), familiarity with inference frameworks (vLLM, llm-d, NVIDIA Dynamo, Ray Serve), strong software engineering in Go or Python, microservices and cloud operations, and defining/maintaining SLOs and observability. Compensation range listed as $167,200–$209,000 (USD, annual).

Join NVIDIA to design and implement robust map-based localization solutions for autonomous driving, working with large real and synthetic datasets. The role requires strong C++ and CUDA skills, experience in computer vision/3D geometry, and familiarity with automotive quality and safety standards such as ISO26262 and MISRA.

Work on designing and optimizing kernels, extensible abstractions for LLM serving engines, JIT domain-specific compilers, and inference runtimes for NVIDIA hardware. Qualifications include a Master's (PhD preferred) or equivalent experience, 6+ years in ML/DL systems development, experience with frameworks such as PyTorch/JAX/TensorFlow/ONNX, and expertise in GPU kernel development and compilers. Base salary range listed as 184,000 USD - 287,500 USD per year.

Role requires designing and optimizing GPU kernels and abstractions for LLM inference, building JIT domain-specific compilers and runtimes, and collaborating across frameworks and GPU architecture teams. Requires a Master's (or equivalent) with 6+ years experience, strong Python and C/C++ skills, deep learning framework experience, and GPU/CUDA kernel expertise. Base salary range: 184,000–287,500 USD/year.

Technical lead role on Salesforce's Public Sector team responsible for end-to-end delivery of AI/LLM-driven and multi-cloud Salesforce solutions (Agentforce, Data Cloud, integrations, automation) for DoD/IC/aerospace customers. Requires 8+ years' hands-on experience, ability to travel ~25–50%, work in classified/unclassified environments, and hold active TS/SCI with Polygraph. Candidate must be a U.S. citizen living on U.S. soil.

Role involves analyzing large-scale telemetry and workload data on GPU-accelerated clusters, producing findings and visualizations, partnering with OS/container/GPU/systems engineers, and building lightweight ML/DL implementations integrated into workflows. Requires 5+ years analyzing complex datasets, strong Python and JavaScript, hands-on experience with telemetry/observability stacks (e.g., Grafana, Elasticsearch, Splunk), and a BS/MS in a technical field or equivalent. Base salary range listed: 152,000 USD - 241,500 USD per year.

Role focused on architecting and implementing next-generation GPU profiling features, building functional and performance models, and creating test plans. Requires a Masters/PhD or equivalent, 3+ years in computer architecture/ASIC design/verification or software development, strong C++ and Python, experience with performance modeling/hardware simulation (ideally SystemC), and familiarity with GPU profiling and AI/HPC workloads. Base salary ranges provided for Levels 4 and 5 (overall range in posting: USD 184,000–356,500).

Sayari seeks a Senior Applied Scientist with 5+ years in applied ML and 2+ years hands-on experience with LoRA and full fine-tuning. The role focuses on fine-tuning small language models, building data collection and labeling strategies for messy, real-world commercial data, evaluating models on agentic workflows, and deploying models to production using cloud ML platforms (e.g., Vertex AI or SageMaker). The position is remote within the United States and lists a target base salary of $185,000–$200,000 USD.

Lead design and delivery of scalable ML solutions (Generative AI, RAG, Graph ML, LLM fine-tuning) and high-performance real-time inference services. Hands-on responsibilities include architecture, microservices, Triton/TensorRT-based inference, Python development, CI/CD, Docker/Kubernetes, cloud deployments, and mentoring engineering teams.

CrowdStrike is hiring a Senior Cloud/Backend Engineer for the Charlotte AI team to build globally distributed, fault-tolerant, highly scalable cloud ecosystems and backend microservices. The role involves working with Go, data pipelines, LLMs (training/implementation), cloud services (AWS), and technologies such as Docker, Kubernetes, Kafka, Cassandra and Elasticsearch. Responsibilities include design, development, testing, deployment and monitoring of services, collaborating across teams, maintaining high code quality and mentoring peers. Position is hybrid out of the NYC office; US-based (remote/NY) candidates are targeted.

Senior Software Systems Engineer (L3/L4) role focused on defining system requirements and performance budgets, developing test and validation strategies (vehicle and component levels), and ensuring safety (SOTIF ISO 21448, ISO 26262) for autonomous driving. Requires 8+ years in safety-critical systems engineering, strong software skills (Python, C++, SQL), and experience with large datasets and ML validation.

Role involves designing and implementing compiler optimization techniques for deep learning graphs, tensor sharding and graph partitioning for distributed training/inference, and code generation for NVIDIA GPU backends using tools such as MLIR, LLVM, and OpenAI Triton. Requires a BS/MS/PhD or equivalent, 4+ years relevant experience, strong C/C++ skills, performance analysis, and knowledge of GPU/accelerator architectures; experience with JAX, CUDA, XLA/TVM/MLIR/LLVM is a plus.

Responsible for designing, building, and operating AI-based features (including LLMs, agent-driven experiences, RAG, and prompt engineering) across frontend and backend stacks. Requires 5+ years of software engineering experience, strong TypeScript/JavaScript and backend skills (Node.js/Go/Java), experience shipping AI/ML integrations in production, and familiarity with observability and telemetry systems.

Work on analyzing deep learning networks and implementing compiler optimization algorithms for AI inference across NVIDIA GPUs. Collaborate with deep learning framework and GPU architecture teams; require 3+ years relevant experience, strong C/C++ and Python skills, and experience with compiler technologies (e.g., MLIR, LLVM, XLA, Triton).

NVIDIA seeks a new college graduate (Master's/PhD or equivalent) to research, design, and optimize high-performance AI and HPC workloads on CPUs and GPUs for financial services. Responsibilities include low-level parallel programming (e.g., CUDA), profiling and eliminating bottlenecks, collaborating with hardware/software teams, and publishing results. Role emphasizes C/C++, algorithms, linear algebra, and experience improving large-scale GPU application performance; locations listed in the US with remote options.

Role for an architect/functional modeling engineer to design, develop, validate, and improve GPU architecture and baseboard component simulation models and platforms. Requires 5+ years experience, strong C/C++ and scripting skills, SystemC/TLM modeling experience, and familiarity with ML frameworks (PyTorch, TensorFlow, LangChain).

Role responsible for improving ML training and inference efficiency across GPU clusters by debugging and optimizing software and infrastructure, building tools and frameworks to detect bottlenecks, collaborating with researchers across domains (LLMs, robotics, autonomous vehicles, video), and working with technologies such as CUDA, NCCL, Nsight, distributed training, cloud platforms, and fast distributed storage.