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.

Researches and develops techniques to accelerate deep learning, machine learning, and AI workloads on modern CPU and GPU architectures. Works with developers and researchers to analyze and optimize complex algorithms, publish findings, and influence hardware and software design. Requires a Masters (or equivalent) and 8+ years of relevant experience with C/C++, CUDA and parallel programming frameworks.

Responsible for designing, implementing, and optimizing high-performance dense linear algebra software on GPUs, leading projects and mentoring engineers. Requires 8+ years experience, a degree in CS/Applied Math or equivalent, strong C++ skills, expertise in kernel generation and JIT, and experience with GPU-related technologies (CUDA) and performance optimization. Base salary range shown: $184,000–$356,500 depending on level.

Role involves partnering with NVIDIA engineering, product, and sales to secure design wins; performing proof-of-concepts and in-depth profiling/optimization of ML/DL models on GPU architectures; working with client ML researchers and engineers; and producing collateral (notebooks, blogs) for finance use-cases. Requires 12+ years of relevant experience and strong Python/C++ and ML/DL framework knowledge.

Work on design and implementation of GPU-accelerated libraries and SDKs for unstructured sparsity in deep learning and HPC. Responsibilities include developing a C++ system for sparse tensor computations, DSL specifications for sparse storage formats, on-demand code generation, enabling frameworks like Python/PyTorch, and performance evaluation and optimization. Required: BS/MS/PhD (or equivalent), 6+ years developing and optimizing high-performance C++ software, strong Python and CUDA skills, and experience with parallel programming (MPI/OpenMP) and sparse compilers (MLIR or TACO).

NVIDIA seeks a Solutions Architect for its AI Software Segment to develop end-to-end AI solutions, support LLMs and multimodal models, and guide customers through data processing, training, evaluation, RL, and model optimization. Requires 5+ years with AI frameworks (PyTorch, JAX, TensorFlow), Python, Linux, experience with distributed training tools (Kubernetes, SLURM), and expertise in GPU-based model training. Location noted as US (remote) and Santa Clara; salary range stated.

Work on JAX core design and performance optimizations for NVIDIA products, collaborating with AI researchers and cross-functional teams. Requires BS or equivalent and 6+ years' relevant experience, proficiency in C/C++ and Python, familiarity with ML frameworks (e.g., PyTorch, TensorFlow), and strong knowledge of CPU/GPU architectures and distributed training. Base salary ranges provided: 184,000–356,500 USD/year depending on level.

Role to partner with NVIDIA engineering, product, and sales to drive design wins in banking and consumer finance by performing proofs-of-concept, profiling and optimizing ML/DL models on NVIDIA GPUs, and building industry-specific collateral. Requires 12+ years as an ML/Software Engineer, BS/MS/PhD or equivalent experience, strong coding in Python and C++, and experience with ML/DL frameworks (TensorFlow, JAX, PyTorch, Spark, Dask). Base salary range stated: 224,000 USD - 356,500 USD per year.

NVIDIA is hiring a research scientist to design and implement AI algorithms and models for general-purpose humanoid robots and embodied agents, develop large-scale training and inference methods, and optimize/deploy models in simulation and on robot hardware. Requires PhD (or equivalent), strong engineering and prototyping skills (PyTorch/JAX/TensorFlow), Python or C++, and experience with LLMs/multimodal/foundation models and/or robot learning and simulation frameworks. Position is US (remote) with Santa Clara listed; salary ranges are provided in the posting.

NVIDIA is hiring a Principal Deep Learning Algorithm Engineer to research and implement inference algorithms, profile and optimize agentic LLM workloads to reduce latency and increase throughput, and design scalable datacenter systems. Requires BS/MS/PhD or equivalent and 15+ years in deep learning/systems, strong Python and C++ skills, and knowledge of computer architecture and GPU/parallel computing. Role is listed as US (Santa Clara) with remote options and a base salary range of 272,000–431,250 USD per year.

Role to scope, design, and implement high-performance numerical dense linear algebra software on GPUs, provide technical leadership, and work with product teams and customers. Requires a PhD or Master’s (or equivalent experience), 5+ years developing production software with CUDA and C++, strong fundamentals in finite precision arithmetic and numerical linear algebra. Locations listed: Germany, Poland, France (also remote).

Senior role to develop scalable library software and optimize high-performance parallel numerical applications across CPU and GPU (CUDA). Requires 5+ years experience, strong C/C++ and software-development skills, performance tuning, benchmarking, and collaboration across distributed teams. Python and familiarity with CI/CD and profiling tools are desirable.

Role involves understanding, profiling, and optimizing DL training and inference on state-of-the-art hardware; collaborating across research and engineering teams; implementing production-quality software and building automation tools. Requires 5+ years experience, strong DL and computer-architecture background, and programming skills in C++ and Python; GPU programming (CUDA/OpenCL) and LLM framework experience are advantageous.

Netguru seeks a Senior Data Scientist (B2B freelance, full-time) to work remotely (CET hours) from asap until the end of 2026 with possible extension. Responsibilities include end-to-end ownership of forecasting and optimization models, production lifecycle management (deployment, monitoring, incident response, performance tuning), and aligning model integration with platform design. Requirements: 7+ years in Data Science, 4+ years in forecasting, expert-level Python, Docker and cloud (AWS or GCP), experience with TensorFlow/PyTorch, neural networks, model lifecycle management (drift detection and rollback). English (C1) required.

Lead engineering and delivery of ML-driven recommendation systems for Mapbox's routing cost engine. Responsibilities include building and optimizing ML/data pipelines, leveraging geospatial algorithms, partnering with data scientists, implementing automated QA and monitoring, mentoring engineers, participating in code/design reviews, and joining an on-call rotation. Requires ~8+ years backend and ML experience, reinforcement learning knowledge, Python, large-scale distributed processing (Hadoop/Spark/Airflow/Dask), and cloud infrastructure experience (AWS).

Remote Senior ML Ops Engineer role focused on maintaining and extending data/ML workflows and platforms that handle high-velocity, large-scale data. Requires minimum 3 years in a data role (data stores, feature engineering, pipelines) and at least 2 years of professional software development experience; key technologies include Python, Kafka, SQL/NoSQL (ClickHouse), cloud (AWS/Azure), Kubernetes and CI/CD.

Design and implement ML models that scale to millions of requests per second, translate business requirements into technical specifications, evaluate and debug models, and provide technical mentorship. Role combines research, engineering, and deployment work in a fully remote, flexible-hours environment. Requires 5+ years professional applied ML experience and experience with real-time, incremental, and online learning.

Lead and architect large-scale, production ML projects from design through deployment and maintenance. Require 7+ years building and maintaining production ML systems with focus on scalability, performance, reliability, and leadership. Familiarity with Kubernetes, containerization, distributed systems, and experience mentoring other ML engineers. Positions listed for Poland, Romania, Austria, and Croatia; role is fully remote with flexible hours.

The role leads the ML Security team responsible for bot, agent, and human fraud/abuse detection, owning execution of roadmaps, architectural and technical decisions, code reviews, mentoring, and delivery from data exploration through production deployment and monitoring. Required: strong ML/data science or engineering background with production ML experience, engineering management or tech lead experience, expertise in high-availability and distributed systems, Python, ML pipelines, MLOps, and strong cross-functional communication. Position is fully remote with flexible hours; all candidates will undergo pre-employment screening and identity verification in their country of residence.

10 fellowship places for 2026 offering 6- or 12-month remote terms (candidate may reside anywhere except sanctioned jurisdictions). Fellows spend ~80% on their own research, receive compute, weekly 1:1 mentorship, and a location-adjusted stipend; deliverables typically include 1–2 papers or a final report. Application materials requested; deadlines April 1 and September 1, 2026.