Senior Software Engineer - Self-Serve Intelligence

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.

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.

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.

Earnest is dedicated to empowering ambitious individuals to make informed financial decisions and create the lives they aspire to lead.Our team, known as Earnies, is passionate about providing borrowers with smarter borrowing solutions that offer a clearer path toward financial empowerment. If you share our enthusiasm for this mission, we invite you to explore the details below and join us in building something exceptional.The Senior Model Risk Manager will report directly to the Head of Credit Risk.In this role, you will:Take ownership of and enhance Earnest’s Model Risk Management framework, ensuring that our credit, loss forecasting, fraud, marketing, and finance models are robust, transparent, and scalable.Conduct independent end-to-end model validations, from conceptual soundness and data quality to performance monitoring and implementation review, providing constructive feedback to modeling teams.Collaborate closely with Data Science and Risk leaders early in the model design process to refine assumptions, enhance methodologies, and uplift modeling standards throughout the organization.Supervise model performance monitoring and proactively identify emerging risks, performance drift, or control deficiencies, ensuring timely and effective remediation.Produce clear, decision-ready validation reports and effectively communicate technical findings to drive impactful business outcomes and sound risk management decisions.Act as a trusted advisor on model governance, enabling Earnest to operate swiftly while maintaining the necessary discipline and controls of a leading lending platform.

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

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.

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.

Join Perplexity as a Research Engineering Manager, where you will spearhead a team of exceptional AI researchers and engineers dedicated to crafting the advanced models that power our innovative products. Our talented team has pioneered some of the most sophisticated models in agentic research, query understanding, and other critical domains that demand precision and depth. As we broaden our user base and expand our product offerings, our proprietary models are increasingly essential for delivering a premium experience to the world's most discerning users.You will explore our extensive datasets of conversational and agentic queries, applying state-of-the-art training methodologies to enhance AI model performance. Through proactive technical and organizational leadership, you will empower your team to create cutting-edge models for the applications that are most significant to our business and our users.

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.

OpenAI is seeking a Performance Modeling Engineer based in San Francisco. This role centers on building and improving models that enhance the performance and efficiency of AI systems. The work directly supports the technical backbone of OpenAI’s products. Key responsibilities Develop and refine models aimed at optimizing the performance of AI systems. Collaborate with engineers and data scientists to tackle technical challenges as they arise. Contribute to projects that improve the efficiency of large-scale AI infrastructure. Role overview This position offers the chance to work on foundational technology that underpins OpenAI’s products. The focus is on practical improvements and close teamwork with technical colleagues to advance the capabilities and efficiency of AI at scale.

OpenAI’s Self-Serve Business Growth team focuses on expanding self-serve product offerings, including Codex and the OpenAI API. The team supports the entire customer journey, from acquisition through retention and expansion, aiming to help teams discover, adopt, and maximize OpenAI products through accessible, scalable experiences. Role overview This Product Manager - Lead for Self-Serve Business Growth role centers on shaping the product strategy for how businesses and teams engage with OpenAI’s self-serve products. The position calls for a strategic, data-driven leader with a strong background in product-led growth and B2B products. Success in this role means identifying high-impact opportunities, collaborating across teams, and ensuring a seamless experience as customers move from initial use to broader adoption. What you will do Develop and define a product strategy for self-serve business growth, covering acquisition, conversion, activation, retention, expansion, and monetization. Pinpoint and prioritize product opportunities that drive team adoption, usage, and retention. Create growth strategies to help teams realize value quickly and deepen their engagement over time. Work closely with core product teams to set roadmap priorities and embed growth opportunities into the product experience. Own goals, metrics, and executive-level communications for self-serve business growth, including progress updates, insights, trade-offs, and investment needs. Collaborate with Sales and Go-To-Market teams to design smooth transitions, upsells, and expansion paths between self-serve and Enterprise tiers. Use customer insights, product analytics, experimentation, and business data to guide strategy and measure results. Influence cross-functional partners across Product, Engineering, Design, Data Science, Sales, Marketing, and Operations. Support building and mentoring a growing team of Product Managers as the self-serve growth function scales. Requirements Significant experience as a Product Manager in product-led growth and B2B products, with a demonstrated ability to influence cross-functional teams and drive strategy using data and customer insights.

About SesameAt Sesame, we envision a transformative future where technology is seamlessly integrated into our lives, enabling computers to perceive, interact, and collaborate in ways that feel genuinely human. Our mission is to create innovative voice agents that become an integral part of daily experiences. Our talented team comprises pioneers from Oculus and Ubiquity6, alongside industry leaders from Meta, Google, and Apple, all bringing extensive expertise in both hardware and software. Join us in pioneering a world where computers are truly alive.Key Responsibilities:Enhance our model serving infrastructure, integrating a diverse range of LLM, speech, and vision models.Collaborate with ML infrastructure and training engineers to develop a fast, cost-efficient, and reliable serving layer for our groundbreaking consumer product.Adapt and extend existing LLM serving frameworks such as VLLM and SGLang, leveraging cutting-edge techniques for high-performance model serving.Partner with the training team to uncover opportunities for accelerating model performance without compromising quality.Implement strategies like in-flight batching, caching, and custom kernels to optimize inference speed.Discover methods to minimize model initialization times while maintaining excellence in quality.

About the OpportunityAt Unity, we are dedicated to fostering a culture of collaboration and innovation. Our dynamic environment allows us to tackle intricate challenges that create significant value for creators and users within our ecosystem.The Vector team is at the forefront of this mission, creating cutting-edge conversion rate (CVR) prediction and market price models that enhance our ad ranking and recommendation systems. These models enable advertisers to engage the right users at optimal moments by accurately assessing engagement and conversion probabilities. By harnessing extensive behavioral data, creative features, and contextual signals, we continually refine our predictions’ relevance and accuracy. This leads to crucial outcomes such as increased user engagement, improved conversion rates, and a better return on ad spend—empowering advertisers to meet their objectives while enhancing user experience.We are on the lookout for an experienced Senior Machine Learning Engineer to spearhead advanced bidding optimization systems that facilitate efficient budget management, goal-driven automated strategies, ongoing enhancements through experimentation, and sustainable growth for Unity Ads.

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.

About MeterAt Meter, we believe that networking is at the heart of technological advancement. We have innovatively unified the entire networking stack and are now on a mission to make it autonomous.Our team is developing a cutting-edge neural network-driven system designed to analyze raw computer networks, enabling us to address all networking challenges. As outlined on Meter.ai, we are creating models within a closed-loop system that utilizes real-time telemetry, logs, and network events to autonomously troubleshoot issues, enhance performance, and resolve challenges.To achieve this, we require not only exceptional models but also robust infrastructure that ensures our models have clean, versioned, and low-latency access to the necessary data throughout training, evaluation, and deployment phases.Why this Role is EssentialEach Meter network deployed in the field serves as a valuable data source for our Models team. However, without meticulous infrastructure design, this data risks becoming fragmented, outdated, or inconsistent. In this role, you will ensure that such pitfalls are avoided. You will be responsible for the core data interface that drives our model development, experimentation, evaluation, and real-time inference.This position is fundamental and offers a significant impact. Your contributions will shape the speed at which we can train new models, the reliability of their evaluations, and their seamless operation across hundreds of real-world networks. You will collaborate closely with modelers to deliver systems that are elegant, scalable, and robust.Your ResponsibilitiesDesign and implement the Models API: a unified interface for accessing training, evaluation, and deployment data across raw, transformed, and feature-engineered layers.Ensure backward compatibility and feature versioning across continually evolving schemas.Develop scalable pipelines to ingest, transform, and serve petabytes of data across Kafka, Postgres, and Clickhouse.Create CI/CD workflows that evolve the API in tandem with changes to the underlying data schema.Facilitate fine-grained querying of historical and real-time data for any network, at any point in time.Help establish and promote the principle of 'smart data, dumb functions': maximizing operations in the data layer to minimize downstream code complexity.Collaborate with modelers to co-design training frameworks that optimize performance.

Role OverviewAt Mariana Minerals, we are on a mission to revolutionize refining processes for critical minerals, playing a pivotal role in the global energy transition. We are in search of a dynamic and driven Process Modeling Engineer who will be integral to this endeavor.In this position, you will take charge of developing, validating, and optimizing heat and material balance models utilizing advanced software such as ASPEN Plus/HYSYS, SysCAD, OLI Studio, or METSIM. You will collaborate closely with R&D, pilot operations, and project execution teams to transform lab and pilot data into robust, scalable process models that are essential for the design of groundbreaking mineral refining facilities.Key ResponsibilitiesCreate both steady-state and dynamic process models to determine heat and material balances for integrated mineral refinery systems using ASPEN, SysCAD, OLI, or METSIM.Automate the sizing of equipment and processes (including reactors, heat exchangers, filters, crystallizers, evaporators, and separators) based on model outputs, linking models to datasheets and other engineering tools.Develop and maintain comprehensive process simulation databases to ensure consistency and traceability among modeling assumptions, test data, and engineering outputs.Calibrate and reconcile models using operational data from pilot plants to ensure model accuracy and predictive validity.Conduct optimization studies to enhance energy recovery, recycling strategies, and material efficiency.Develop dynamic models for validating PLC and DCS programming while assessing buffer sizing throughout the design process.Integrate process models with CAPEX and OPEX estimation tools to streamline techno-economic model development.Document modeling methodologies and results, ensuring clear technical communication for design reviews, techno-economic assessments, and regulatory submissions.

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.