Senior Model Risk Manager

At Databricks, we are dedicated to empowering data teams to tackle some of the most challenging issues of our time—from realizing the future of transportation to speeding up medical innovations. We achieve this by developing and maintaining the premier data and AI infrastructure platform, allowing our clients to leverage profound data insights to enhance their operations. Our Model Serving product equips organizations with a cohesive, scalable, and governed platform for deploying and overseeing AI/ML models, spanning traditional ML to specialized large language models. It provides real-time, low-latency inference, governance, monitoring, and lineage capabilities. With the rapid rise of AI adoption, Model Serving stands as a fundamental component of the Databricks platform, enabling clients to operationalize models efficiently and cost-effectively at scale. As a Senior Engineer, your role will be pivotal in transforming both the product experience and the underlying infrastructure of Model Serving. You will design and create systems enabling high-throughput, low-latency inference across CPU and GPU workloads, influence architectural strategies, and work closely with platform, product, infrastructure, and research teams to deliver an exceptional serving platform.

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.

At Databricks, we are driven by our commitment to empower data teams in tackling the world's most challenging problems — from transforming transportation solutions to accelerating medical advancements. Our mission revolves around constructing and maintaining the world's premier data and AI infrastructure platform, enabling our clients to harness deep data insights for enhanced business outcomes.Foundation Model Serving represents the API product designed for hosting and serving advanced AI model inference, catering to both open-source models like Llama, Qwen, and GPT OSS, as well as proprietary models such as Claude and OpenAI GPT. We welcome engineers who have experience managing high-scale operational systems, including customer-facing APIs, Edge Gateways, or ML Inference services, even if they do not have a background in ML or AI. A passion for developing LLM APIs and runtimes at scale is essential.As a Staff Engineer, you will play a pivotal role in defining both the product experience and the underlying infrastructure. You will be tasked with designing and building systems that facilitate high-throughput, low-latency inference on GPU workloads with cutting-edge models. Your influence will extend to architectural direction, working closely with platform, product, infrastructure, and research teams to deliver an exceptional foundation model API product.The impact you will have:Design and implement core systems and APIs that drive Databricks Foundation Model Serving, ensuring scalability, reliability, and operational excellence.Collaborate with product and engineering leaders to outline the technical roadmap and long-term architecture for workload serving.Make architectural decisions to enhance performance, throughput, autoscaling, and operational efficiency for GPU serving workloads.Contribute directly to critical components within the serving infrastructure, from systems like vLLM and SGLang to developing token-based rate limiters and optimizers, ensuring seamless and efficient operations at scale.Work cross-functionally with product, platform, and research teams to transform customer requirements into dependable and high-performing systems.Establish best practices for code quality, testing, and operational readiness while mentoring fellow engineers through design reviews and technical support.Represent the team in inter-departmental technical discussions, influencing Databricks’ wider AI platform strategy.

At Databricks, we are dedicated to empowering data teams to tackle the most challenging global issues—whether it's transforming transportation or speeding up medical advancements. We achieve this by constructing and managing the world's leading data and AI infrastructure platform, enabling our clients to leverage deep data insights for business enhancement. The Model Serving product at Databricks offers enterprises a cohesive, scalable, and governed platform for deploying and managing AI/ML models—from conventional ML to sophisticated, proprietary large language models. It facilitates real-time, low-latency inference while providing governance, monitoring, and lineage capabilities. As AI adoption surges, Model Serving becomes a central component of the Databricks platform, allowing customers to operationalize models efficiently and cost-effectively. As a Senior Engineering Manager, you will lead a team responsible for both the product experience and the underlying infrastructure of Model Serving. This role involves shaping user-facing features while architecting for scalability, extensibility, and performance across CPU and GPU inference. You will collaborate closely with various teams across the platform, product, infrastructure, and research domains.

Lyft’s Self-Serve Intelligence team builds the systems that help riders and drivers resolve issues on their own. Part of the Safety & Customer Care organization, this group focuses on backend services, APIs, and AI-powered products that let customers get help without waiting for an agent. The team’s work includes AI Assist (such as AI Agents), automations, and self-service workflows, all designed to make support fast and reliable. Role overview As a Senior Software Engineer on this team, the main responsibility is to design, build, deploy, and maintain backend systems and AI-driven tools that handle customer problems automatically. These solutions use Generative AI and automation to deliver scalable, dependable self-service experiences for millions of Lyft riders and drivers. What you will do Design and develop backend services and APIs for AI-powered self-service products Build and maintain AI Agents and automation tools that resolve customer issues without agent involvement Oversee the full development lifecycle: system design, prototyping, deployment, and ongoing operations Work closely with product managers, designers, data scientists, and operations teams to deliver robust solutions Focus on reliability, scalability, and operational excellence in all systems Location This role is based in San Francisco, CA.

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.

ABOUT BASETENAt Baseten, we are at the forefront of enabling transformative AI solutions for some of the world's leading companies, including Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. Our innovative platform combines cutting-edge AI research, adaptable infrastructure, and developer-friendly tools to facilitate the production of advanced models. Recently, we celebrated our rapid growth with a successful $300M Series E funding round from notable investors like BOND, IVP, Spark Capital, Greylock, and Conviction. We invite you to join our dynamic team and contribute to the evolution of AI product deployment.THE ROLEAs a Senior Software Engineer specializing in Model Training at Baseten, you will play a pivotal role in constructing the infrastructure essential for the large-scale training and fine-tuning of foundational AI models. Your responsibilities will include designing and implementing distributed training systems, optimizing GPU utilization, and establishing scalable pipelines that empower Baseten and our clientele to adapt models with efficiency and reliability. This role demands a high level of technical expertise and hands-on involvement: you will be responsible for critical components of our training stack, collaborate with product and infrastructure teams to identify customer needs, and drive advancements in scalable training infrastructure.EXAMPLE WORK:Training open-source models that surpass GPT-5 capabilities for a leading digital insurerExploring specialized, continuously learning models as the future of AIOverview of our training documentationResearch initiatives we've undertakenRESPONSIBILITIESDesign, construct, and sustain distributed training infrastructures for large foundation modelsDevelop scalable pipelines for fine-tuning and training across diverse GPU/accelerator clustersEnhance training performance through optimization of algorithms and infrastructureCollaborate closely with cross-functional teams to align technical solutions with business objectivesStay abreast of advancements in the field of machine learning and AI to continually improve our training processes

Join our dynamic Machine Learning Infrastructure team as a Senior AI Infrastructure Engineer, where you will play a pivotal role in designing and constructing platforms that ensure the scalable, reliable, and efficient serving of Large Language Models (LLMs). Our innovative platform supports a range of cutting-edge research and production systems, catering to both internal and external applications across diverse environments.The ideal candidate will possess a solid foundation in machine learning principles coupled with extensive experience in backend system architecture. You will thrive in a collaborative environment that bridges research and engineering, working diligently to provide seamless experiences for our customers and accelerating innovation across the organization.

As a Senior Staff Software Engineer specializing in Model LifeCycle at Crusoe, you will play a vital role in shaping the future of software solutions that optimize and enhance our innovative operations. You will lead complex projects, mentor junior engineers, and collaborate with cross-functional teams to deliver high-impact results.

At Sciforium, we're at the forefront of AI infrastructure innovation, dedicated to developing cutting-edge multimodal AI models and a proprietary, high-efficiency model serving platform. With significant multi-million-dollar backing and direct collaboration from AMD, including hands-on support from AMD engineers, our team is rapidly expanding to construct the comprehensive stack that fuels leading-edge AI models and real-time applications.About the RoleJoin us in a unique opportunity to architect and spearhead the development of Sciforium's next-generation model serving platform, the powerhouse that will deliver a multimodal, high-performance foundation model to market. As a senior technical leader, you will not only craft core components but also mentor and guide fellow engineers, shaping engineering direction, standards, and quality of execution.You'll delve into the entire AI stack: from GPU kernels and quantized execution paths to distributed serving, scheduling, and the APIs that drive real-time AI applications. If you relish deep systems work, thrive on ownership, and aspire to lead engineers in constructing foundational AI infrastructure, this role places you at the heart of Sciforium's mission and growth.Your ResponsibilitiesSteer the technical direction of the model serving platform, overseeing architectural decisions and engineering execution.Develop core serving components such as execution runtimes, batching, scheduling, and distributed inference systems.Create high-performance C++ and CUDA/HIP modules, including custom GPU kernels and memory-optimized runtimes.Collaborate with ML researchers to transition new multimodal models into production while ensuring low-latency, scalable inference.Construct Python APIs and services that make model capabilities accessible to downstream applications.Mentor and assist other engineers through code reviews, design discussions, and direct technical support.Lead performance profiling, benchmarking, and observability initiatives across the inference stack.Guarantee high reliability and maintainability through rigorous testing, monitoring, and adherence to engineering best practices.Diagnose and resolve intricate issues spanning GPU, runtime, and service layers.

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 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.

At Crusoe, we are on a mission to accelerate the convergence of energy and intelligence. We are building a powerful engine that enables individuals to innovate boldly with AI, all while upholding principles of scalability, speed, and sustainability.Join us in spearheading the AI revolution through sustainable technology. At Crusoe, you will be at the forefront of meaningful innovation, making a significant impact while collaborating with a team dedicated to shaping the future of responsible, transformative cloud infrastructure.About the Role:As a Senior Software Engineer on the Model Lifecycle team, you will play a pivotal role in developing a managed platform that supports the entire application development lifecycle, with an emphasis on harnessing the power of Machine Learning models, particularly Large Language Models (LLMs).Your Responsibilities:Design and maintain systems for fine-tuning large foundational models (SFT, PEFT, LoRA, adapters), ensuring multi-node orchestration, checkpointing, failure recovery, and cost-effective scaling.Create and manage end-to-end training pipelines for Large Language Models.Implement components for distillation and reinforcement learning pipelines, focusing on preference optimization, policy optimization, and reward modeling.Develop and sustain the core agent execution infrastructure.Implement features for dataset, model, and experiment management, emphasizing versioning, lineage, evaluation, and reproducible fine-tuning.Collaboration and Impact:Collaborate closely with Senior Engineers, Principal Engineers, and various product and platform teams to implement systems abstractions and APIs.Engage in technical discussions surrounding training runtimes, scheduling, storage, and overall model lifecycle management.Bring 4-5+ years of industry experience, demonstrating a strong track record of successfully leading a diverse portfolio of initiatives.Participate in and contribute to the open-source LLM ecosystem.This position involves taking significant ownership of core system components.Your Qualifications:Engineering Fundamentals:Bachelor's degree in Computer Science, Engineering, or a related discipline.Proven experience in software engineering with a focus on AI models and machine learning.

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.

Waymo is a pioneering company in autonomous driving technology, dedicated to becoming the world’s most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has established the Waymo Driver—The World’s Most Experienced Driver™—with a mission to enhance mobility access and save lives lost in traffic accidents. The Waymo Driver powers our fully autonomous ride-hailing service and can be integrated across various vehicle platforms and applications. Having completed over ten million rider-only trips, our technology has driven more than 100 million miles on public roads and tens of billions in simulations across over 15 U.S. states.The Perception team is responsible for developing systems that learn the spatial-temporal representations and semantic meanings of the environment surrounding our autonomous vehicles (AVs). We collaborate closely with downstream teams to optimize and integrate our work into the Waymo Driver, conduct research to solve real-world challenges, and work alongside research teams at Alphabet. With access to millions of miles of diverse driving data from various sensors, we empower engineers like you to (1) create methods for efficient continuous learning from extensive real-world data, (2) develop scalable models and training methodologies, (3) analyze real-world behaviors to create systems that can navigate complexities, and (4) optimize models for both onboard and offboard hardware.In this hybrid role, you will report to a Technical Lead Manager.

Preference Model develops reinforcement learning environments that mirror the complexity of real-world tasks. The company focuses on building diverse RL tasks and detailed reward structures, aiming to push the boundaries of artificial intelligence. The founding team brings experience from developing data infrastructure and datasets for Claude at Anthropic, and Preference Model works closely with top AI research labs. Role overview The Senior Software Engineer - Reinforcement Learning Environments position centers on designing and delivering RL environments that challenge and improve current AI models. This role involves leading complex projects, including multi-step workflows and realistic stakeholder interactions, within a large codebase. Engineers work directly with the founders and a small, collaborative team, delivering environments used for training advanced models at partner labs. The position provides significant autonomy, regular feedback, and support for professional development. What you will do Design, build, and iterate on reinforcement learning tasks, taking them from concept through evaluation. Lead the development of sophisticated environments, focusing on complex workflows and coding standards. Interact with coding agents, review their outputs, and identify subtle failures. Analyze whether issues stem from model limitations or environment design, then redesign tasks to reveal deeper failure modes. Contribute to building and maintaining the core infrastructure and tools for the environments team. Mentor junior engineers as the team expands. Location This role is based in San Francisco.

Benchling creates software tools for scientists and biotech companies, supporting research and development across the globe. The platform serves more than 200,000 scientists, including teams at organizations like Sanofi and Moderna, as well as academic research labs. By connecting experiments, structured data, and AI-powered insights, Benchling works to reduce the time it takes for discoveries to reach real-world applications. Role overview This Software Engineer position focuses on integrating advanced scientific AI models into the Benchling platform. The main responsibility is to build a scalable system for hosting and managing scientific models, while also developing frameworks that allow model creators to bring their solutions into the Benchling environment. What you will do Develop and maintain a platform that supports scientific AI models at scale. Create frameworks that make it easier for model developers to contribute to Benchling. Experiment with new technologies to improve model integration and performance. Work closely with internal teams and external partners to deliver solutions. Help shape how scientists design molecules and apply AI in their research workflows. Location This role is based in San Francisco, CA.

Role overview Meter Inc. is developing tools to capture and preserve the expertise of network engineers. The team’s goal is to build systems that document how experts diagnose network issues, making it possible for future models to manage networks with less manual effort. This work will help Meter support many customer networks while reducing the need for direct engineer intervention. What makes this work unique Network engineering lacks the structured archives found in software development. While Git and GitHub record software decisions, the reasoning behind network troubleshooting often disappears once a problem is fixed. This role centers on building a structured, searchable system for network operations, a kind of GitHub for network engineering. The system will capture network state, expert observations, and the logic behind key decisions. Your first 90 days First 30 days: Meet with network engineers to learn their workflows. Study what effective diagnostic documentation looks like and identify the necessary data. Review telemetry (ClickHouse), configurations (Postgres), and support history (Salesforce). By 60 days: Deliver a working annotation interface. Network engineers should be able to review past support tickets, view the network’s state during incidents, and record their reasoning. The tool should be practical and encourage regular use. By 90 days: Network engineers will be able to create training data independently. Initial model benchmarks from your pipeline will be live, showing how your work improves the process. Technical stack TypeScript React Go GraphQL Kafka Postgres Collaboration This role works closely with Meter’s co-founder and CEO, who will help guide the product roadmap and set priorities. Location This position is based in San Francisco.

Zyphra is an innovative artificial intelligence company located in the heart of San Francisco, California.The Opportunity:Join our dynamic team as a Research Engineer - Audio & Speech Models, where you will play a pivotal role in advancing Zyphra’s Audio Team. You will be instrumental in developing cutting-edge open-source text-to-speech and audio models. Your contributions will span the full spectrum of the model training process, from data collection and processing to the design of innovative architectures and training approaches.Your Responsibilities:Conduct large-scale audio training operationsOptimize the performance of our training infrastructureCollect, process, and evaluate audio datasetsImplement architectural and methodological improvements through rigorous testingWhat We Seek:A strong research mindset with the ability to navigate projects from ideation to implementation and documentation.Proficiency in rapid prototyping and implementation, allowing for swift experimentation.Effective collaboration skills in a fast-paced research environment.A quick learner who is eager to embrace and implement new concepts.Excellent communication abilities, enabling you to contribute to both research and engineering tasks at scale.Preferred Qualifications:Expertise in training audio models, such as text-to-speech, ASR, speech-to-speech, or emotion recognition.Experience with training audio autoencoders.Solid understanding of signal processing, particularly in audio.Familiarity with diffusion models, consistency models, or GANs.Experience with large-scale (multi-node) GPU training environments.Strong understanding of experimental methodologies for conducting rigorous tests and ablations.Interest in large-scale, parallel data processing pipelines.Competence in PyTorch and Python programming.Experience contributing to large, established codebases with rapid adaptation.