Software Engineer, Inference Deployment

About Our TeamJoin the Inference team at OpenAI, where we leverage cutting-edge research and technology to deliver exceptional AI products to consumers, enterprises, and developers. Our mission is to empower users to harness the full potential of our advanced AI models, enabling unprecedented capabilities. We prioritize efficient and high-performance model inference while accelerating research advancements.About the RoleWe are seeking a passionate Software Engineer to optimize some of the world's largest and most sophisticated AI models for deployment in high-volume, low-latency, and highly available production and research environments.Key ResponsibilitiesCollaborate with machine learning researchers, engineers, and product managers to transition our latest technologies into production.Work closely with researchers to enable advanced research initiatives through innovative engineering solutions.Implement new techniques, tools, and architectures that enhance the performance, latency, throughput, and effectiveness of our model inference stack.Develop tools to identify bottlenecks and instability sources, designing and implementing solutions for priority issues.Optimize our code and Azure VM fleet to maximize every FLOP and GB of GPU RAM available.You Will Excel in This Role If You:Possess a solid understanding of modern machine learning architectures and an intuitive grasp of performance optimization strategies, especially for inference.Take ownership of problems end-to-end, demonstrating a willingness to acquire any necessary knowledge to achieve results.Bring at least 5 years of professional software engineering experience.Have or can quickly develop expertise in PyTorch, NVidia GPUs, and relevant optimization software stacks (such as NCCL, CUDA), along with HPC technologies like InfiniBand, MPI, and NVLink.Have experience in architecting, building, monitoring, and debugging production distributed systems, with bonus points for working on performance-critical systems.Have successfully rebuilt or significantly refactored production systems multiple times to accommodate rapid scaling.Are self-driven, enjoying the challenge of identifying and addressing the most critical problems.

OverviewAt Pulse, we are revolutionizing the way data infrastructure operates by addressing the critical challenge of accurately extracting structured information from intricate documents on a large scale. Our innovative document understanding technique merges intelligent schema mapping with advanced extraction models, outperforming traditional OCR and parsing methods.Located in the heart of San Francisco, we are a dynamic team of engineers dedicated to empowering Fortune 100 enterprises, YC startups, public investment firms, and growth-stage companies. Backed by top-tier investors, we are rapidly expanding our footprint in the industry.What sets our technology apart is our sophisticated multi-stage architecture, which includes:Specialized models for layout understanding and component detectionLow-latency OCR models designed for precise extractionAdvanced algorithms for reading-order in complex document structuresProprietary methods for table structure recognition and parsingFine-tuned vision-language models for interpreting charts, tables, and figuresIf you possess a strong passion for the convergence of computer vision, natural language processing, and data infrastructure, your contributions at Pulse will significantly impact our clients and help shape the future of document intelligence.

About Our TeamJoin OpenAI’s dynamic Inference team, where we empower the deployment of cutting-edge AI models, including our renowned GPT models, advanced Image Generation capabilities, and Whisper, across diverse platforms. Our mission is to ensure these models are not only high-performing and scalable but also available for real-world applications. Collaborating closely with our Research team, we’re committed to bringing the next generation of AI innovations to fruition. As a compact, agile team, we prioritize delivering an exceptional developer experience while continuously pushing the frontiers of artificial intelligence.As we expand our focus into multimodal inference, we are building the necessary infrastructure to support models that process images, audio, and other non-text modalities. This work involves tackling diverse model sizes and interactions, managing complex input/output formats, and ensuring seamless collaboration between product and research teams.About The RoleWe are seeking a passionate Software Engineer to aid in the large-scale deployment of OpenAI’s multimodal models. You will join a small yet impactful team dedicated to creating robust, high-performance infrastructure for real-time audio, image, and various multimodal workloads in production environments.This position is inherently collaborative; you will work directly with researchers who develop these models and with product teams to define novel interaction modalities. Your contributions will enable users to generate speech, interpret images, and engage with models in innovative ways that extend beyond traditional text-based interactions.Key Responsibilities:Design and implement advanced inference infrastructure for large-scale multimodal models.Optimize systems for high-throughput and low-latency processing of image and audio inputs and outputs.Facilitate the transition of experimental research workflows into dependable production services.Engage closely with researchers, infrastructure teams, and product engineers to deploy state-of-the-art capabilities.Contribute to systemic enhancements, including GPU utilization, tensor parallelism, and hardware abstraction layers.You May Excel In This Role If You:Have a proven track record of building and scaling inference systems for large language models or multimodal architectures.Possess experience with GPU-based machine learning workloads and a solid understanding of the performance dynamics associated with large models, particularly with intricate data types like images or audio.Thrive in a fast-paced, experimental environment and enjoy collaborating with cross-functional teams to drive impactful results.

Join Cartesia as an Inference EngineerAt Cartesia, our vision is to create the next evolution of AI: an interactive, omnipresent intelligence that operates seamlessly across all environments. Currently, even the most advanced models struggle to continuously analyze a year's worth of audio, video, and text data—comprising 1 billion text tokens, 10 billion audio tokens, and 1 trillion video tokens—much less perform these tasks on-device.We are at the forefront of developing the model architectures that will make this a reality. Our founding team, who met as PhD candidates at the Stanford AI Lab, pioneered State Space Models (SSMs), a groundbreaking framework for training efficient, large-scale foundation models. Our talented team merges deep expertise in model innovation and systems engineering with a design-focused product engineering approach, enabling us to build and launch state-of-the-art models and user experiences.Supported by leading investors such as Index Ventures and Lightspeed Venture Partners, along with contributions from Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks, and others, we are fortunate to be guided by numerous exceptional advisors and over 90 angel investors from diverse industries, including some of the world’s foremost experts in AI.About the RoleWe are actively seeking an Inference Engineer to propel our mission of creating real-time multimodal intelligence.Your ImpactDevelop and implement a low-latency, scalable, and dependable model inference and serving stack for our innovative foundation models utilizing Transformers, SSMs, and hybrid models.Collaborate closely with our research team and product engineers to efficiently deliver our product suite in a fast, cost-effective, and reliable manner.Construct robust inference infrastructure and monitoring systems for our product offerings.Enjoy substantial autonomy in shaping our products and directly influencing how cutting-edge AI is integrated across diverse devices and applications.What You BringAt Cartesia, we prioritize strong engineering skills due to the complexity and scale of the challenges we tackle.Proficient engineering skills with a comfort level in navigating intricate codebases, and a commitment to producing clean, maintainable code.Experience in developing large-scale distributed systems with strict performance, reliability, and observability requirements.Proven technical leadership, capable of executing and delivering results from zero to one amidst uncertainty.A background in or experience with inference pipelines, machine learning, and generative models.

About Our TeamThe Inference team at OpenAI is dedicated to translating our cutting-edge research into accessible, transformative technology for consumers, enterprises, and developers. By leveraging our advanced AI models, we enable users to achieve unprecedented levels of innovation and productivity. Our primary focus lies in enhancing model inference efficiency and accelerating progress in research through optimized inference capabilities.About the RoleWe are seeking talented engineers to expand and optimize OpenAI's inference infrastructure, specifically targeting emerging GPU platforms. This role encompasses a wide range of responsibilities from low-level kernel optimization to high-level distributed execution. You will collaborate closely with our research, infrastructure, and performance teams to ensure seamless operation of our largest models on cutting-edge hardware.This position offers a unique opportunity to influence and advance OpenAI’s multi-platform inference capabilities, with a strong emphasis on optimizing performance for AMD accelerators.Your Responsibilities Include:Overseeing the deployment, accuracy, and performance of the OpenAI inference stack on AMD hardware.Integrating our internal model-serving infrastructure (e.g., vLLM, Triton) into diverse GPU-backed systems.Debugging and optimizing distributed inference workloads across memory, network, and compute layers.Validating the correctness, performance, and scalability of model execution on extensive GPU clusters.Collaborating with partner teams to design and optimize high-performance GPU kernels for accelerators utilizing HIP, Triton, or other performance-centric frameworks.Working with partner teams to develop, integrate, and fine-tune collective communication libraries (e.g., RCCL) to parallelize model execution across multiple GPUs.Ideal Candidates Will:Possess experience in writing or porting GPU kernels using HIP, CUDA, or Triton, with a strong focus on low-level performance.Be familiar with communication libraries like NCCL/RCCL, understanding their importance in high-throughput model serving.Have experience with distributed inference systems and be adept at scaling models across multiple accelerators.Enjoy tackling end-to-end performance challenges across hardware, system libraries, and orchestration layers.Be eager to join a dynamic, agile team focused on building innovative infrastructure from the ground up.

Join our innovative team at Anthropic as a Software Engineer specializing in Cloud Inference Safeguards. In this role, you will play a crucial part in developing and enhancing the systems that ensure the robustness and security of our cloud-based inference services. You will collaborate with cross-functional teams to design, implement, and maintain scalable solutions that meet our high standards for reliability and performance.

P-1285 About This Role Join Databricks as a Staff Software Engineer specializing in GenAI inference, where you will spearhead the architecture, development, and optimization of the inference engine that powers the Databricks Foundation Model API. Your role will be crucial in bridging cutting-edge research with real-world production requirements, ensuring exceptional throughput, minimal latency, and scalable solutions. You will work across the entire GenAI inference stack, including kernels, runtimes, orchestration, memory management, and integration with various frameworks and orchestration systems. What You Will Do Take full ownership of the architecture, design, and implementation of the inference engine, collaborating on a model-serving stack optimized for large-scale LLM inference. Work closely with researchers to integrate new model architectures or features, such as sparsity, activation compression, and mixture-of-experts into the engine. Lead comprehensive optimization efforts focused on latency, throughput, memory efficiency, and hardware utilization across GPUs and other accelerators. Establish and uphold standards for building and maintaining instrumentation, profiling, and tracing tools to identify performance bottlenecks and drive optimizations. Design scalable solutions for routing, batching, scheduling, memory management, and dynamic loading tailored to inference workloads. Guarantee reliability, reproducibility, and fault tolerance in inference pipelines, including capabilities for A/B testing, rollbacks, and model versioning. Collaborate cross-functionally to integrate with federated and distributed inference infrastructure, ensuring effective orchestration across nodes, load balancing, and minimizing communication overhead. Foster collaboration with cross-functional teams, including platform engineers, cloud infrastructure, and security/compliance professionals. Represent the team externally through benchmarks, whitepapers, and contributions to open-source projects. What We Look For A BS/MS/PhD in Computer Science or a related discipline. A solid software engineering background with 6+ years of experience in performance-critical systems. A proven ability to own complex system components and influence architectural decisions from conception to execution. A deep understanding of ML inference internals, including attention mechanisms, MLPs, recurrent modules, quantization, and sparse operations. Hands-on experience with CUDA, GPU programming, and essential libraries (cuBLAS, cuDNN, NCCL, etc.). A strong foundation in distributed systems design, including RPC frameworks, queuing, RPC batching, sharding, and memory partitioning. Demonstrated proficiency in diagnosing and resolving performance bottlenecks across multiple layers (kernel, memory, networking, scheduler).

Join our dynamic team at Perplexity as an AI Inference Engineer, where you will be at the forefront of deploying cutting-edge machine learning models for real-time inference. Our tech stack includes Python, Rust, C++, PyTorch, Triton, CUDA, and Kubernetes, providing you with a chance to work on large-scale applications that make a real impact.Key ResponsibilitiesDesign and develop APIs for AI inference that cater to both internal and external stakeholders.Conduct benchmarking and identify bottlenecks within our inference stack to enhance performance.Ensure the reliability and observability of our systems while promptly addressing any outages.Investigate innovative research and implement optimizations for LLM inference.

Join the Fluidstack TeamAt Fluidstack, we’re pioneering the infrastructure for advanced intelligence. We collaborate with leading AI laboratories, governmental entities, and major corporations—including Mistral, Poolside, and Meta—to deliver computing solutions at unprecedented speeds.Our mission is to transform the vision of Artificial General Intelligence (AGI) into a reality. Driven by our purpose, our dedicated team is committed to building state-of-the-art infrastructure that prioritizes our customers' success. If you share our passion for excellence and are eager to contribute to the future of intelligence, we invite you to be part of our journey.Role OverviewThe Inference Platform team at Fluidstack is at the forefront of addressing the cost and latency challenges associated with frontier AI. You will play a crucial role in managing the serving layer that connects our global accelerator supply with the production workloads of our clients, which include LLM serving frameworks, KV cache infrastructure, and Kubernetes orchestration across multiple data centers.This hands-on individual contributor role combines elements of distributed systems, model optimization, and serving infrastructure. You will oversee the entire lifecycle of inference deployments for leading AI labs, striving for enhancements in throughput, cost-efficiency, and response times, while also influencing the architectural decisions that guide Fluidstack’s deployment strategies.

Baseten develops infrastructure and tools that help AI companies deploy and scale inference. Teams at organizations like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer rely on Baseten to bring advanced machine learning models into production. The company recently secured a $300M Series E from investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Role overview This Software Engineer - GPU Inference position joins the founding team for Baseten Voice AI in San Francisco. The team focuses on building production-ready Voice AI systems, bringing open-source voice models into real-world use for clients in productivity, customer service, healthcare conversations, and education. The work shapes how people interact with technology through voice, creating broad impact across industries. In this role, the engineer leads the internal inference stack that powers Voice AI models. Responsibilities include guiding the product roadmap and driving engineering execution. Collaboration is a key part of the job, working closely with Forward Deployed Engineers, Model Performance Engineers, and other technical groups to advance Voice AI capabilities. Sample projects and initiatives The world's fastest Whisper, with streaming and diarization Canopy Labs selects Baseten for Orpheus TTS inference Partnering with the Core Product team to build an orchestration framework for a multi-model voice agent Working with the Training Platform team to support continuous training of voice models Designing a developer-friendly API and SDK for self-service adoption of Baseten Voice AI products

ABOUT BASETENAt Baseten, we empower the leading AI companies of today, including Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer, by providing essential inference capabilities. Our unique blend of applied AI research, adaptable infrastructure, and intuitive developer tools enables innovators at the cutting edge of AI to seamlessly transition advanced models into production. With our recent success in securing a $300M Series E funding round, backed by notable investors such as BOND, IVP, Spark Capital, Greylock, and Conviction, we're on an exciting growth trajectory. Join our team and contribute to the platform that engineers rely on to launch AI-driven products.THE ROLEAs an Applied AI Inference Engineer at Baseten, you'll collaborate closely with clients to design, develop, and implement high-performance AI applications using our platform. You will guide customers through the entire process, from initial concept to deployment, transforming vague business objectives into dependable, observable solutions that meet defined quality, latency, and cost metrics.This position is ideal for innovative engineers eager to gain insight into how modern organizations scale AI adoption. You will thrive if you enjoy a multifaceted role that intersects product development, software engineering, performance optimization, and direct customer engagement.It’s essential to note that this position requires hands-on coding and software development, while also encompassing elements of product management, technical customer success, and pre-sales engineering.EXAMPLE INITIATIVESExplore insights from our Forward Deployed Engineering team through these blog posts: Forward Deployed Engineering on the frontier of AIThe fastest, most accurate Whisper transcriptionDeploy production-ready model servers from Docker imagesDeploy custom ComfyUI workflows as APIs...

At Inferact, we are on a mission to establish vLLM as the premier AI inference engine, revolutionizing AI progress by making inference both more accessible and efficient. Our founding team consists of the original creators and key maintainers of vLLM, positioning us uniquely at the nexus of cutting-edge models and advanced hardware.Role OverviewWe are seeking a passionate inference runtime engineer eager to explore and expand the frontiers of LLM and diffusion model serving. As models evolve and grow in complexity with new architectures like mixture-of-experts and multimodal designs, the demand for innovative solutions in our inference engine intensifies. This role places you at the heart of vLLM, where you will enhance model execution across a variety of hardware platforms and architectures. Your contributions will have a direct influence on the future of AI inference.

Who are we?At Cohere, our mission is to elevate intelligence to benefit humanity. We specialize in training and deploying cutting-edge models for developers and enterprises focused on creating AI systems that deliver extraordinary experiences such as content generation, semantic search, retrieval-augmented generation, and intelligent agents. We view our work as pivotal to the broad acceptance of AI technologies.We are passionate about our creations. Every team member plays a vital role in enhancing our models' capabilities and the value they provide to our customers. We thrive on hard work and speed, always prioritizing our clients' needs.Cohere is a diverse team of researchers, engineers, designers, and more, all dedicated to their craft. Each individual is a leading expert in their field, and we recognize that a variety of perspectives is essential to developing exceptional products.Join us in our mission and help shape the future of AI!Why this role?Are you excited about architecting high-performance, scalable, and reliable machine learning systems? Do you aspire to shape and construct the next generation of AI platforms that enhance advanced NLP applications? We are seeking talented Members of Technical Staff to join our Model Serving team at Cohere. This team is responsible for the development, deployment, and operation of our AI platform, which delivers Cohere's large language models via user-friendly API endpoints. In this role, you will collaborate with multiple teams to deploy optimized NLP models in production settings characterized by low latency, high throughput, and robust availability. Additionally, you will have the opportunity to work directly with customers to create tailored deployments that fulfill their unique requirements.

About This Role Join Databricks as a Software Engineer focused on GenAI inference, where you will play a pivotal role in designing, developing, and enhancing the inference engine that drives our Foundation Model API. Collaborating at the intersection of research and production, you will ensure our large language model (LLM) serving systems are optimized for speed, scalability, and efficiency. Your contributions will span the entire GenAI inference stack, from kernels and runtimes to orchestration and memory management. What You Will Do Participate in the design and implementation of the inference engine, collaborating on a model-serving stack tailored for large-scale LLM inference. Work closely with researchers to integrate new model architectures or features such as sparsity, activation compression, and mixture-of-experts into the engine. Optimize latency, throughput, memory efficiency, and hardware utilization across GPUs and other accelerators. Build and maintain tools for instrumentation, profiling, and tracing to identify bottlenecks and inform optimization efforts. Develop scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads. Ensure reliability, reproducibility, and fault tolerance in inference pipelines, including A/B launches, rollback, and model versioning. Integrate with federated and distributed inference infrastructure, orchestrating across nodes, balancing load, and managing communication overhead. Engage in cross-functional collaboration with platform engineers, cloud infrastructure, and security/compliance teams. Document and share insights, contributing to internal best practices and open-source initiatives as appropriate.

ABOUT BASETENAt Baseten, we are at the forefront of AI innovation, providing critical inference solutions for leading AI companies like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. Our platform combines advanced AI research, adaptable infrastructure, and intuitive developer tools, empowering organizations to deploy state-of-the-art models effectively. With rapid growth and a recent $300M Series E funding round backed by top-tier investors including BOND, IVP, Spark Capital, Greylock, and Conviction, we invite you to join our mission in building the platform of choice for engineers delivering AI products.THE ROLE:As a member of Baseten’s Model Performance (MP) team, you will play a pivotal role in ensuring our platform’s model APIs are not only fast and reliable but also cost-effective. Your primary focus will be on developing and optimizing the infrastructure that supports our hosted API endpoints for cutting-edge open-source models. This role involves working with distributed systems, model serving, and enhancing the developer experience. You will collaborate with a small, dynamic team at the intersection of product development, model performance, and infrastructure, defining how developers interact with AI models on a large scale.RESPONSIBILITIES:Design, develop, and maintain the Model APIs surface, focusing on advanced inference features such as structured outputs (JSON mode, grammar-constrained generation), tool/function calling, and multi-modal serving.Profile and optimize TensorRT-LLM kernels, analyze CUDA kernel performance, create custom CUDA operators, and enhance memory allocation patterns for maximum efficiency across multi-GPU setups.Implement performance improvements across various runtimes based on a deep understanding of their internals, including speculative decoding, guided generation for structured outputs, and custom scheduling algorithms for high-performance serving.Develop robust benchmarking frameworks to evaluate real-world performance across diverse model architectures, batch sizes, sequence lengths, and hardware configurations.Enhance performance across runtimes (e.g., TensorRT, TensorRT-LLM) through techniques such as speculative decoding, quantization, batching, and KV-cache reuse.Integrate deep observability mechanisms (metrics, traces, logs) and establish repeatable benchmarks to assess speed, reliability, and quality.

About AnthropicAt Anthropic, our mission is to develop AI systems that are safe, interpretable, and controllable. We believe in harnessing AI for the greater good of our users and society at large. Our dynamic team comprises dedicated researchers, engineers, policy experts, and business leaders who collaborate to create beneficial AI systems.About the RoleThe Cloud Inference team is responsible for scaling and optimizing Claude to cater to a vast array of developers and enterprise clients across platforms such as AWS, GCP, Azure, and future cloud service providers (CSPs). We manage the complete lifecycle of Claude on each cloud platform—from API integration and intelligent request routing to inference execution, capacity management, and daily operations.Our engineers wield significant influence, driving multiple key revenue streams while optimizing one of Anthropic's most valuable resources—compute power. As we expand to additional cloud providers, the intricacies of efficiently managing inference across diverse platforms with varying hardware, networking frameworks, and operational models grow substantially. We seek engineers adept at navigating these variances, developing strong abstractions that are effective across providers, and making informed infrastructure choices that keep us cost-effective at scale.Your contributions will enhance the operational scale of our services, expedite our capacity to launch cutting-edge models and innovative features to clients across all platforms, and ensure our large language models (LLMs) adhere to stringent safety, performance, and security standards.

At Hover, we empower individuals to design, enhance, and safeguard their cherished properties. Utilizing proprietary AI technology built on over a decade of real property data, we provide answers to pressing questions such as “What will it look like?” and “What will it cost?” Homeowners, contractors, and insurance professionals depend on Hover to receive fully measured, accurate, and interactive 3D models of any property—achieved through a smartphone scan in mere minutes.We are driven by curiosity, purpose, and a collective commitment to our customers, communities, and each other. At Hover, we believe the most innovative ideas stem from diverse perspectives, and we take pride in fostering an inclusive, high-performance culture that encourages growth, accountability, and excellence. Supported by leading investors like Google Ventures and Menlo Ventures, and trusted by industry leaders including Travelers, State Farm, and Nationwide, we are transforming how people perceive and interact with their environments.Why Join Hover?At Hover, 3D models are not just a feature; they are the essence of our product. Each scan and data point we process empowers homeowners, insurers, and contractors to make informed, data-driven decisions. We are seeking a Software Engineer who has a passion for geometry, automation, and making a tangible impact in the real world. In this role, you will design and implement systems that convert customer-captured imagery into meticulously accurate 3D models, enhancing the scalability and precision of Hover’s modeling pipeline. You will work collaboratively with designers and engineers across frontend, backend, computer vision, and DevOps to bring innovative capabilities to fruition, blending technical expertise with strong communication and cross-functional collaboration.The 3D Modeling Pipeline team develops the tools essential for our in-house operations to transform customer-captured scans into highly detailed, accurate 3D models of buildings. This team is also responsible for creating the pipeline and systems that process 3D data through both automated and manual steps, as well as exporting data into customer-facing formats.Your Contributions Will Include:Owning and evolving backend systems that convert raw scan data into exact 3D models, ensuring timely delivery to key ecosystem partners like Xactimate and Cotality.Building and refining internal modeling tools that enable teams to efficiently generate, validate, and optimize high-quality 3D data.Collaborating with machine learning and computer vision engineers to implement new algorithms into production, bridging research with practical applications.Enhancing customer and partner experiences by improving how Hover’s 3D outputs integrate with downstream workflows and external platforms.Promoting innovation and ongoing enhancement across our modeling pipeline.

OpenAI is seeking a Software Engineer in San Francisco to focus on improving productivity by optimizing model performance. This position centers on developing solutions that make machine learning models more efficient and effective. Role overview This role involves working closely with teams across different functions to identify and address areas where model performance can be improved. The aim is to deliver changes that have a measurable impact on both systems and workflows. What you will do Collaborate with engineers and other specialists to enhance model efficiency Develop and implement solutions that improve the effectiveness of machine learning systems Contribute to projects that streamline processes and drive productivity gains Impact Your work will help shape improvements in how models operate and how teams at OpenAI achieve their goals. The changes you help deliver will support more effective use of resources and better outcomes for the organization.

At Databricks, we are dedicated to empowering data teams to tackle the most challenging problems in the world — from realizing the future of transportation to fast-tracking medical innovations. We accomplish this by developing and operating the premier data and AI infrastructure platform, enabling our customers to harness profound data insights for business enhancement. Our Model Serving product equips organizations with a cohesive, scalable, and governed solution for deploying and managing AI/ML models — ranging from traditional machine learning to intricate proprietary large language models. It ensures real-time, low-latency inference, governance, monitoring, and lineage. As the adoption of AI surges, Model Serving stands as a fundamental component of the Databricks platform, allowing customers to operationalize models at scale with robust SLAs and cost efficiency. In the role of Staff Engineer, you will significantly influence both the product experience and the core infrastructure of Model Serving. Your responsibilities will include designing and constructing systems that facilitate high-throughput, low-latency inference across CPU and GPU workloads, steering architectural strategies, and collaborating extensively with platform, product, infrastructure, and research teams to create an exceptional serving platform.