Deep Learning Jobs

Scale AI

The Human Frontier Collective (HFC) aims to bring together top minds to shape the future of AI, focusing on the people who teach AI systems. The collective comprises researchers, academics, and domain leaders from over 70 fields, with most members holding doctorates. HFC members contribute to shaping how frontier AI systems are built, evaluated, and governed, and have co-authored significant research publications. The fellowship offers participation in high-impact AI/ML projects, opportunities to publish research, and access to an exclusive expert network.

Scale AI

The Machine Learning Engineer role at Scale focuses on applying advanced techniques in generative AI, computer vision, reinforcement learning, and agentic AI to enhance production environments for Scale's products and customer experience. Leveraging robust internal infrastructure and extensive datasets, ML engineers drive product improvements. The Public Sector team specifically deploys cutting-edge models for mission-critical government systems, working with large language models and computer vision. This includes developing agentic systems for complex challenges, building agent frameworks, and creating evaluation tools. Efforts also involve advancing reinforcement learning for LLMs and training advanced computer vision models for defense applications, including large-scale fine-tuning and developing generalizable foundation models.

$235k - $294k

Scale AI

Scale works with leading AI labs to accelerate progress in GenAI research, focusing on LLM post-training and evaluation. This role is on the evaluation pod, building benchmarks and diagnosing model failure modes in text and multimodal modalities. You will develop rigorous evaluations to reveal where and why frontier models fail, collaborating with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with foundation model labs to translate failure analysis into technical and strategic input for next-generation generative AI models.

$166k - $207k

Scale AI

Scale is seeking a Senior Machine Learning Engineer to own the application of generative AI, agentic AI, computer vision, and reinforcement learning to mission-critical production problems. In this role, you will tackle challenges without established solutions, proposing and building architectures, and ensuring their performance in customer-dependent environments. The Public Sector Machine Learning team focuses on deploying advanced models to government systems, with a primary emphasis on agentic systems built on large language models. This includes developing agents for complex operational and planning tasks, integrating retrieval pipelines and production APIs, managing state for long-running tasks, performing geospatial reasoning, and creating evaluation tooling. The role also involves applying reinforcement learning and computer vision for defense applications, including evaluation, labeling efficiency, and multimodal model training. As a Senior MLE, you will have design authority over a capability area, making final decisions on team patterns and ensuring they function under real-world constraints like classified environments, limited compute, and strict correctness requirements.

$235k - $294k

Scale AI

Scale AI accelerates the development of AI systems by providing the data, infrastructure, and tooling that power the most advanced models in the world. As AI rapidly evolves from static models to dynamic, agentic systems, Scale is building the foundational research, evaluation methodologies, and agent/RL infrastructure that will define this next era. You’ll join a high-impact research organization driving advances in large-language models, post-training, evaluation, and agentic/RL environments, helping shape how next-generation AI is built, measured, and deployed.

$290k - $363k

Scale AI

Scale works with leading AI labs to accelerate GenAI research by providing high-quality data. This role focuses on optimizing data curation and evaluation to enhance LLM capabilities in text and multimodal modalities. You will develop novel methods to improve the alignment and generalization of large-scale generative models, collaborate with researchers and engineers on best practices in data-driven AI development, and partner with foundation model labs to provide technical and strategic input on next-generation generative AI models.

$166k - $207k

Scale AI

The Human Frontier Collective (HFC) is a unique and exclusive expert network that brings together researchers, academics, and domain leaders from over 70 fields to shape the future of AI. With over 90% of members holding doctorates, the collective focuses on how frontier AI systems are built, evaluated, and governed. Members participate in high-impact AI/ML projects, collaborate on research publications, and gain access to a vibrant community network. This role offers opportunities to work on projects such as building AI safety guardrails, optimizing PyTorch models, and evaluating ML code, with the potential to co-author technical reports and research papers.

As an ML Infrastructure Engineer, you will play a pivotal role in building and optimizing the reliable, high-performance ML platform that powers recommendations on X. We're looking for exceptional engineers who are passionate about our mission and have a strong desire to make a meaningful impact.

$180k - $440k

The Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns over 10 real-time deep learning models that must constantly evolve to stay ahead of fraudsters. Each ML improvement translates directly into dollar impact for Stripe and its users. The team's models also power the Radar product suite that tens of thousands of businesses use to screen payments and manage fraud. In this role, you will own ML work across the full lifecycle: researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers. You will have opportunities to optimize Stripe’s most intensive ML models, and opportunities to ship 0-to-1 products from scratch.

The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce, helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe. The team is growing and expanding into multiple new areas, building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for over 50 global payment methods. You will own ML problems from conception to production, analyzing new fraud and abuse patterns, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance. Your work may include developing transformer architectures for payment data, building graph-powered intelligence, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections.

Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants. Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers.

The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce, helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe. The team is growing and expanding into multiple new areas, including building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for over 50 global payment methods. You will own major ML systems and initiatives from problem definition to production, finding where ML can make the biggest difference, designing the approach, building and shipping models and the systems around them, and continuously improving them as data, users, and attackers change. Your work may involve protecting businesses from abuse across the customer lifecycle, building adaptive and customizable fraud systems for Stripe's largest users, or extending risk intelligence to over 50 global payment methods. Across all these, you'll use modern approaches such as transformer models over payment data, graph intelligence across consumers, and LLMs to detect emerging attacks. You will have opportunities to ship 0→1 products and improve mission-critical ML models operating at Stripe scale, often driving several efforts in parallel and setting the technical bar for the team.

Abundant is building the NVIDIA of training data, recognizing the critical need for scaling data as AI models advance beyond supervised learning to reinforcement learning and learning from experience. Our founding team brings deep expertise from leading AI organizations, with a proven track record in deploying large-scale deep learning models, training state-of-the-art self-driving models, and managing massive data pipelines. We are at the forefront of innovation, addressing the scarcity of training data by advancing simulation, synthetic data generation, and learning from experience, which are essential for achieving AGI, ASI, and physical intelligence. We partner with top AI labs and enterprises to provide the data necessary for models to move from general knowledge to domain expertise and from chatbots to agents.

$168k - $168k

NVIDIA is seeking strategic, ambitious, hard-working, and creative individuals for its 2027 Deep Learning Computer Architecture internships. This 12-week full-time program offers an excellent opportunity to expand your career and gain hands-on experience with industry-leading Deep Learning Computer Architecture teams. Interns will work on projects with a measurable impact on NVIDIA's business, tackling complex challenges in AI and digital twins that are transforming major industries.

$0k - $0k

AssemblyAI is a leading Voice AI company developing foundational speech models that power the next generation of voice applications. We operate at a significant scale, processing millions of hours of audio daily and serving billions of end-user experiences. This is a growth-stage opportunity within a capital-efficient company that offers significant ownership and impact. We foster a flat culture where employees are deeply involved in the details and can drive their work forward without bureaucratic hurdles. If you are passionate about Voice AI and want to solve interesting problems at scale, this is the place for you.

$270k - $310k

The Robotic Visual Perception and Learning research group at NVIDIA is seeking highly motivated PhD students for research internships. This role focuses on pushing the boundaries of computer vision, robotics, and machine learning with real-world impact. Interns will have the opportunity to publish their work in top-tier conferences and collaborate with experienced mentors, with carefully crafted projects tailored to their expertise.

$0k - $0k

NVIDIA is seeking research interns with a strong background in applying modern AI methods to re-imagine technology across Earth System Simulation and Analytics. This role involves research on applied topics such as generative data assimilation, hybrid climate simulation, full model emulation of climate model components, regional-to-global high-resolution AI autoregressive weather prediction, and dynamical downscaling. We are looking for creative, independent, and self-starting individuals to help invent the future of AI in climate science. The ideal candidate will have strong computer science skills with knowledge of physical domain sciences, or strong domain science skills with sufficient technical expertise. Join a diverse research group that tackles challenging interdisciplinary problems, publishes at top venues, and drives real impact on NVIDIA products and global climate prediction.

$0k - $0k

NVIDIA's TensorRT team is seeking talented engineers to advance high-performance AI inference on NVIDIA GPUs. This role focuses on developing software that accelerates state-of-the-art generative AI models for local and enterprise deployments. You will tackle challenging problems in GPU code generation, just-in-time compilation, and inference optimization to deliver high performance across diverse models and hardware configurations. As a Senior Software Engineer, you will design and implement advanced GPU inference software, translating a deep understanding of GPU execution into efficient code generation and measurable improvements in generative AI inference.

$152k - $288k

Abnormal AI is seeking a Senior Software Engineer to join the Detection Team, focused on building advanced technology to identify and stop email and cloud-based attacks. In this role, you will enhance the speed, responsiveness, stability, and confidence of feature development for the ML and Data Science teams. The ideal candidate possesses a first-principles approach to scalable, customer-centric solutions, a drive to solve meaningful problems, and an ownership-oriented outlook, with the ability to iterate quickly and autonomously on novel challenges.

$180k - $259k

NVIDIA is seeking a Senior Compiler Engineer to join our GPU Software organization. In this role, you will be instrumental in delivering features and improvements that maximize the potential of NVIDIA hardware across diverse computational workloads, including deep learning, scientific computing, autonomous vehicles, and gaming. Our compiler organization plays a critical role in every GPU NVIDIA produces. You will focus on the CUDA compiler front end, developing language features, diagnostics, and capabilities that empower both human developers and AI agents to create correct and high-performance GPU code. This position offers the opportunity to expand your expertise into other areas of the compiler stack, such as the LLVM-based optimizer, code generation, and MLIR.

$152k - $242k