Lead AI Engineer on the Data Platform & Services team responsible for designing scalable agentic orchestration frameworks, building NLQ/self-service natural language query solutions, developing LLM and prompt evaluation frameworks, and integrating AI orchestration with Python/SQL/Snowflake data pipelines. Annual base salary range: $151,500–$215,500 USD.

Research and develop techniques to GPU-accelerate deep learning, machine learning and AI workloads; analyze and optimize complex AI and HPC algorithms on modern CPU/GPU architectures; publish and present findings and collaborate with hardware, software and research teams. Requires an advanced computational degree (or equivalent), 15+ years' relevant experience, fluency in C/C++, parallel programming experience (CUDA, OpenACC, OpenMP, MPI, pthreads), and deep knowledge of CPU/GPU architecture and low-level performance optimization.

Work on design and training of generative, imitation, and reinforcement learning models to improve planning and reasoning for autonomous driving; build, pre-train, and fine-tune LLM/VLM/VLA systems for real-world deployment; explore data generation strategies; collaborate to deploy production-grade, safety-critical ML integrated with vehicle firmware. Requires strong Python and C++ skills, experience with deep learning frameworks, and demonstrated deployment of ML at scale. Education/experience: PhD +4 years or MS (or equivalent) +6 years in a related technical field.

New college grad (2026) position building ML and AI systems for circuit design and design automation (VLSI/ASIC/EDA). Work includes silicon data analysis, SPICE correlation, circuit optimization, design-space exploration, and integrating models with ML and visualization tools. Requires a Master's or PhD (or equivalent experience) and proficiency in Python and C++.

Veeva is hiring a Technical Product Manager with 5+ years of enterprise software/product or engineering experience (at least 2 years on AI/ML) to design, prototype, and deliver production AI Agent applications. The role requires Python coding, experience with AI frameworks/APIs, data flow design, model integration, LLMs, and close collaboration with engineering and UX; life sciences experience is a plus.

Design, implement, and operate end-to-end production ML systems including large-scale data pipelines, model training, serving, monitoring, and automated retraining. Work cross-functionally to improve ranking, recommendation, search relevance, advertising systems, and LLM-powered products. Requires 3+ years building and deploying ML systems, strong software engineering skills (Python/Java/Go), experience with ML frameworks (PyTorch/TensorFlow), and familiarity with large-scale data and real-time systems.

Work involves designing, building, and deploying production-grade ML models and infrastructure (recommendation, search/ranking, ads, content/user understanding, LLM applications). Requires 3+ years building and operating ML systems, strong programming (Python/Java/Go), experience with ML frameworks (PyTorch/TensorFlow), ML algorithms (XGBoost, Transformers, GNNs), and large-scale data/pipeline tooling. Base salary range listed: $216,700–$303,400 USD per year.

Owner of developer tooling and AI DevX platforms at Ramp: evaluate and integrate AI models/tools, build platforms that enable AI-driven engineering, gather developer feedback, and grow vendor partnerships. Role expects ~8+ years of software engineering experience, Python proficiency, strong backend/infrastructure and data systems expertise, and familiarity with AI coding tools and agents.

Join Ramp's Applied AI team to develop full-stack AI applications (AI Agents, RAG, structured extraction), build infrastructure for LLM inference and fine-tuning, and create internal tools to scale AI across the company. Role is based in Ramp's New York (HQ) or San Francisco offices; relocation support to NYC or SF is available.

Forward Deployed Engineer role focused on building agentic experiences and internal/external agents, working closely with customers and cross-functional teams to scope, prioritize, and ship scalable production software. Prefers candidates with 2+ years of software engineering experience and extensive experience building with and for agents.

Lead technical strategy and architecture for Docker's containerized agent platform and the open-source Docker Agent project. Responsibilities include designing agent runtimes, extending the cagent project, defining evaluation frameworks, ensuring reliability and observability, rapid prototyping, and mentoring senior engineers. Requires 10+ years software engineering experience (including 3+ years technical leadership), strong Go proficiency (preferred), AI/LLM and containerization expertise, and is only available to candidates located in London.

Senior technical pre-sales role acting as an AI subject matter expert and account-level technical lead ("technical CTO") for GitLab customers. Responsibilities include leading technical discovery, architecture design, POC/POV engagements, workshops, cross-functional collaboration, and creating reusable technical assets; base salary range for US residents provided.

Work on Docker Agent (cagent) to design and implement containerized runtimes for AI agents. Responsibilities include expanding cagent, agent runtime development, RAG integration, evaluation frameworks, rapid prototyping, and community engagement. Role is remote-first with a flexible hybrid schedule but candidates must be located in London or a reasonable commuting distance. Docker does not offer visa sponsorship for this role.

Procurify is hiring an Engineering Manager, AI Engineering to lead a team of AI engineers in designing, building, and operating LLM-powered applications, RAG systems, agentic workflows, and AI platform capabilities. The role is a player‑coach position requiring hands-on technical leadership, experience shipping production AI/ML services, and expertise with tools like Python, Azure/OpenAI or AWS Bedrock, Databricks, Kubernetes, and vector databases. The position is remote-first and open to work-from-Canada candidates; listed base salary range is 184,000–205,000 CAD per year.

Join NVIDIA Research Singapore to research, design and accelerate generative AI models (including LLMs, visual generation, and robotics). Responsibilities include publishing research, collaborating across teams, mentoring interns and transferring technology to product groups. Requires 2 years' experience, strong research record (publications at top conferences), proficiency in Python (prototype), and familiarity with ML frameworks such as PyTorch; C++/CUDA and parallel programming are a plus.

Lead research and technical collaborations to advance quantum algorithms and applications, contribute to NVIDIA quantum products (including AI methods, simulations, and digital twins), drive partnerships with supercomputing centers and QPU builders, and publish/represent work at conferences. Requires PhD or equivalent and 5+ years' experience.

Work with science and research institutes in Japan to promote and deploy NVIDIA GPU-accelerated solutions for machine learning and generative AI. Responsibilities include leading research collaborations, designing architectures and demos, creating and running proofs of concept, onboarding developers to NVIDIA AI platforms (NIMs, NeMo), and presenting technical topics. Requires 5+ years' experience in generative AI/deep learning, strong coding skills (Python, C/C++, Bash, Linux), and excellent Japanese and business-level English communication.

NVIDIA seeks a Solutions Architect (Data Science) in ANZ to enable GSIs and AI consultancies on generative AI and LLM solutions (Nemo/NIMs), build GenAI full-stack solutions, run PoCs/demos, and support GPU-accelerated deployments. Requires deep expertise in generative AI/LLMs, ML/DL frameworks, Python, GPU/parallel computing, container orchestration and 5+ years' related experience.

NVIDIA Research is hiring a recent PhD graduate to work on machine learning systems spanning hardware, software, and infrastructure. Responsibilities include analyzing efficiency, scaling, and resilience challenges, developing systems solutions, co-designing ML algorithms and systems, collaborating across teams, and publishing research. Required background includes strong systems knowledge (OS, distributed systems, data management, networking, cloud, or architecture) and experience with C/C++/Python and experimental R&D.

Join NVIDIA's Developer Technology team to research and develop GPU acceleration techniques for deep learning, machine learning, and other AI domains. Role involves in-depth analysis and optimization of complex AI and HPC algorithms, collaboration with research and engineering teams, and publishing findings. Requires a Master’s (or equivalent), 8+ years relevant experience, programming fluency in C/C++, and experience with parallel programming (CUDA, OpenACC, OpenMP, MPI, pthreads) and low-level performance optimizations. Locations listed in the US with hybrid/remote options; posted salary ranges provided by level.