Senior Ml Operations Engineer Jobs

P-926 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products. Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking multiple senior engineers to drive enhancements to our Search Quality. In 2026, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. Key Responsibilities Drive the development and deployment of ML based search and discovery relevance models and systems integrated with Databricks' products and services. Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration. Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience. Contribute to building a robust framework for evaluating search ranking improvements - both offline and online. What We’re Looking For BS+ (M.S. or PhD preferred) in Computer Science, or a related field. 5+ years experience developing search relevance systems at scale in production or in high-impact research environments. Experience applying LLM to search relevance Experience in one or more of the following: Query understanding NLP Text mining Recommendations Personalization Discovery Conversational AI Strong understanding of computer science fundamentals. Contributions to well-used open-source projects. Why Join Us? At Databricks, we are building state-of-the-art AI solutions that red

NVIDIA is seeking a Senior Software Engineer to help us develop distributed storage services for AI/ML. In this role you will work closely with the broader NVIDIA team to design and build a reliable, scalable, and efficient storage-as-a-service tailored to AI applications that can be deployed anywhere and scale without limitations. This service supports the whole NVIDIA critical business from graphics drivers to autonomous vehicles to deep learning frameworks. To achieve this goal, we are looking for an engineer with a deep understanding of distributed systems, outstanding design skills, and a track record in building and delivering large-scale distributed services. What you will be doing: Leading the overall architecture and design of our distributed storage service optimized for AI/ML Develop and maintain distributed, robust and scalable Go programs deployed to state of the art open-source ecosystems, including Kubernetes. Develop and maintain user-space applications, containers, Go-bindings, and CLI tools. Building features for a distributed storage service to enhance availability and reliability for large-scale deployments Engaging and collaborating with NVIDIA Research, Computing, Product teams, cross-functional teams, and external customers to deliver Cloud services. Automating distributed storage service end-to-end, including deployment, management, and monitoring What we need to see: Bachelor’s of Science in Computer Science, or related field (or equivalent experience) with 8+ years of industry experience Strong background in developing distributed systems involving Golang, Kubernetes, and Cloud Service Provider integrations Strong track record of delivering distributed services in a variety of distributed computing environments Experience in i

About the Team Agentic Foundations, part of New Verticals ML, builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash, and proves it through flagship agent experiences such as the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations make our agents better, and real-world agent usage surfaces the gaps that drive our next foundations work. About the Role We’re looking for a Senior Machine Learning Engineer to join Agentic Foundations with a primary focus on Vertical Agent Development. You will own the continuous improvement of our flagship agents, starting with the Ask Assistant, using an eval-driven loop to make them faster, more cost-efficient, and more reliable for millions of customers. You will also help us bring this approach to new agentic experiences for Merchants and Dashers, turning promising ideas into production-ready agents. This is a chance to work at the intersection of LLMs, agents, post-training, and evals on problems that ship. You will partner closely with engineers working on agentic memory, agent-compatible product representations, and post-training of small language models (SLMs), so that what we learn from production agents feeds directly back into our foundations, and what we build there makes our agents better. You will report into the engineering lead on our New Verticals AI/ML team. We expect this role to be hybrid with some time in-office and some time remote. You’re excited about this opportunity because you will… Raise the quality of our Consumer agents: build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context, and measure the impact on real customer tasks. Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can replace or augment larger LLMs. Launch new agents: take ag

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. The Customer Experience Engineering Team builds the internal and external technologies that scale Snowflake’s global support and sales organizations. We empower our technical experts by providing the advanced tools they need to resolve complex issues and drive customer success. Our team specializes in software engineering, data-driven decisions, ML, and LLM-based solutions . We build production-grade systems to automate manual processes and augment the capabilities of our technical staff. Our current focus includes: LLMs : Developing and deploying LLM and agent-based architectures for streamlining troubleshooting Scalable Evaluations : Implementing large-scale evaluations to ensure the quality and reliability of our internal and external tools Process Automation : Designing intelligent workflows that eliminate bottlenecks and allow our experts to focus on the most technical aspects of the Snowflake platform Incident discovery: using embeddings, LLMs, clustering, and agents to detect potential widespread issues more quickly Now, the team is growing, and we are looking for a Software Engineer to join us. In this role, you will work closely with the state of the art LLM models, fine-tune them, develop agents, apply various clusterings, summarizations, embeddings, and so on. Ev

The Autonomy Behaviors team is responsible for producing our vehicle’s driving trajectory. As a Principal ML Engineer, you will lead the technical development of machine learning algorithms for our next-generation ML Planner. Your impact will range from delivering innovations for immediate milestones to pioneering exploratory projects. You will collaborate closely with teams specializing in Foundation Models, Perception, Simulation, and Safety Validation, in influencing our autonomy stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company. In this role, you will: Set the technical direction of ML development for the future of our Planner Push the frontiers of imitation learning, reinforcement learning, and model scaling to develop new ML models for our onboard Planner Identify and drive force-multipliers for ML development, including metrics, data, and training frameworks Develop new methods to leverage Zoox’s Foundation Models into the ML Planner Provide technical mentorship to the broader group of ML developers at Zoox Collaborate with engineers across Autonomy Software, Simulation, System Design and Mission Assurance, and other departments to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Proven track record of technical leadership in large-scale AI organizations Significant experience with training and deploying Deep Learning models to production Fluency in C++ or Fluency in Python with a basic understanding of C++ Extensive experience with programming and algorithm design-Strong mathematics skills, 10+ years of experience Bonus Qualifications Conference or Journal publications in Machine Learning or Robotics related v

Position Overview As a Senior Staff Software Engineer on Diligent's Platform AI team, based out of our technical center of excellence in Vancouver, you will drive the architecture and technical direction of the services that bring AI research and models into production across Diligent's global product suite. You will partner with AI researchers, product managers, and Staff Engineers as peers — leading through influence across the Platform AI organization rather than through reporting authority, on secure, scalable AWS Serverless solutions that translate ML and LLM research into commercial-quality, production systems. Key Responsibilities Drive technical direction and architecture for ML/LLM systems across multiple AI platform teams, partnering with Staff Engineers rather than managing ICs in isolation. Design secure, scalable, fault-tolerant AI/ML solutions using AWS Serverless technology — event-driven, highly observable, with infrastructure as code. Evaluate and prototype new AI technologies (LLM frameworks, model providers, agentic tooling) to guide build-vs-buy decisions across the organization. Serve as the go-to contact for architecture problems and questions across the Platform AI org, and regularly present and guide architecture discussions department-wide. Mentor Staff Software Engineers and Applied Scientists through constructive code review and design partnership. Collaborate with Applied Scientists and Product teams to productize AI capabilities, translating multi-quarter AI strategy into execution roadmaps and representing engineering's perspective to senior leadership. Design, build, and scale search and retrieval systems — including OpenSearch and semantic/vector search — that power discovery, retrieval-augmented generation, and other AI-driven product features. Lead through ambiguity, helping teams navigate undefined problems to successful delivery. Required Experience/Skills 10-15+ years of professional experience developing and owning high-scale, s

About the Role Datadog’s Technology Partnerships team builds and scales partnerships with the technology companies that matter most to our customers from AI/ML platforms to data platform providers. As a Technology Partner Manager, you will own the full, 360-degree relationship with a portfolio of strategic Technology Partners, with a particular focus on cutting-edge AI/ML and Data Platform companies. You’ll be the connective tissue across three pillars of partnership: co-build (joint product and integration roadmaps), co-market (joint awareness and demand generation), and co-sell (joint pipeline and revenue). This is a highly cross-functional, relationship-driven role that requires equal comfort in a product roadmap discussion, a marketing planning session, and a sales enablement meeting. At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do 360° Relationship Ownership: Own the end-to-end partner relationship for a portfolio of AI/ML and Data Platform technology partners, serving as the primary point of contact and trusted advisor across product, engineering, marketing, and sales stakeholders on both sides. Co-Build: Partner closely with Datadog Product and Engineering teams to identify, prioritize, and drive joint integration roadmaps; ensure integrations are built, launched, and continuously improved to meet customer needs. Co-Market: Design and execute joint marketing programs including webinars, blog posts, customer case studies, launch campaigns, and event collaboration with Datadog product marketing and partner marketing teams to build awareness and demand for joint solutions. Co-Sell: Build and execute go-to-market and co-sell plans with Datadog channel team, partner field and sales teams; enable Datadog’s global sales organization and partner sellers o

Technology Partnerships Team at Datadog About the Role Datadog’s Technology Partnerships team builds and scales partnerships with the technology companies that matter most to our customers from AI/ML platforms to data platform providers. As a Technology Partner Engineer, you will be the technical bridge between Datadog and our partner developer community, acting as a consultant who helps partners build world-class monitoring solutions on the Datadog platform. You will be the primary technical contact for our partners, guiding them through the entire integration lifecycle from initial architectural design to final product integrations, and launching on the Datadog Marketplace. This is a unique role that combines deep technical troubleshooting with high-level consulting and platform advocacy, working directly with external developers and seeing your contributions immediately reflected in the Datadog ecosystem. What You’ll Do Technical Advisory: Act as the technical lead for partners, advising on OAuth flows, log pipelines, OpenTelemetry, and MCP-based and API-based integrations and configurations. Co-Build: Partner closely with Datadog Partner, Product and Engineering teams to identify, prioritize, and drive joint integration roadmaps; ensure integrations are built, launched, and continuously improved to meet customer needs. Architectural & Code Review: Perform architectural assessments and deep-dive code reviews for partner integrations and marketplace repositories, ensuring they meet Datadog’s quality rubric. Technical Troubleshooting: Solve complex technical challenges for partners via dedicated technical consultations. Platform Advocacy: Identify friction points in the Datadog platform and partner with internal Product and Engineering teams to build a better developer experience. Documentation & Tracking: Maintain public-facing developer documentation including blog posts, and internal tracking systems to ensure transparency and scale. Market Awarenes

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions. Responsibilities: Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersect

About the role Chime’s Machine Learning Platform (MLP) team builds and operates the infrastructure, tooling, and developer experience that powers machine learning across the company. We enable data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently. As a Senior Software Engineer on the Machine Learning Platform team, you will design and build scalable systems spanning traditional machine learning and emerging AI workloads, including model training, feature computation, real-time inference, foundation-model access, evaluation, and agentic orchestration. You’ll work at the intersection of distributed systems, cloud infrastructure, applied machine learning, and AI product engineering. This role focuses on creating secure, reliable, and reusable platform capabilities that help teams choose the right approach, from conventional predictive models to LLM-powered and multi-step agentic systems, while maintaining strong standards for evaluation, observability, governance, privacy, and cost efficiency. In this role, you can expect to Design, build, and operate scalable ML and AI infrastructure on AWS. Design and operate shared platform capabilities for LLM and agentic workloads, including model access, prompt and configuration lifecycle, retrieval, tool integration, state management, and workflow orchestration. Build evaluation frameworks for non-deterministic AI systems, including offline benchmarks, regression testing, online quality signals, human feedback, and failure analysis. Establish observability, reliability, and governance for models and agents, covering traces, model and prompt versions, tool calls, latency, token usage, quality, safety, privacy, and cost. Help teams make principled architecture decisions across traditional ML, LLM-powered applications, and agentic workflows, and contribute to the platform’s technical roadmap. Build distributed training, batch inference, and large-scale processing systems using

About the position Showpad is building a Revenue Intelligence engine that transforms raw signals—CRM data, email threads, transcripts, and content engagement—into prescriptive AI guidance. We are looking for a technical Product Manager to own the unified data intelligence layer that powers this engine. You will define how we capture and model data, and critically, determine the best retrieval and inference strategies (Search, RAG, Knowledge Graphs, or Vector Similarity) for every use case. Key Responsibilities Data Modeling & Architecture : Define the canonical model for entities (deals, contacts, skills) and establish the retrieval strategy—choosing between keyword, dense vector, hybrid, or graph traversal based on latency and accuracy. Search & RAG Ownership : Own the end-to-end RAG pipeline (chunking, embeddings, indexing). Write technical specs for retrieval layers in Meeting Prep and Roleplay AI, and define evaluation metrics (Recall@k, MRR). Revenue Intelligence Roadmap : Drive the vision from "trusted foundation" to "prescriptive AI." Own the product logic for Deal Health and Winning Behavior models. Platform Enablement : Act as the internal PM champion for the data platform, providing semantic primitives, event taxonomies, and data contracts to other product squads. Discovery & Strategy : Conduct deep customer discovery and stay ahead of LLM/Search trends, translating fuzzy business questions into scoped ML problems. Required Skills Must haves 5+ years in PM: at least 2 years specifically owning data platforms, search, analytics, or ML products. Retrieval Expertise: Hands-on experience shipping RAG pipelines or search systems. You understand the trade-offs between BM25, vector search, and reranking. Data Technicality : Fluency with Datalake architectures (Delta Lake, BigQuery, etc.) and strong entity-relationship modeling skills. ML Fluency : Ability to define features, label designs, and evaluation metrics. You can review a technic

Senior Applied Scientist Prolific You'll join Prolific's Supply team, which is responsible for the health and growth of our participant marketplace, the supply side of our two-sided marketplace connecting participants and researchers. This is a senior role on an established Supply Data Science team, focused on marketplace dynamics—in particular driving participant supply, demand, and quality through advanced optimization techniques and recommendation algorithm expertise. You will work closely with existing specialists in this space to design, validate, and scale high-impact algorithmic systems that optimize study matching and participant experience. As a senior scientist, you will lead complex technical directions and help define the right problems to solve, driving both strategy and implementation. The role The Applied Scientist role is new to Prolific. It combines data science, ML, and statistics to build practical systems that drive real business outcomes. This isn't a pure research role, although you may explore papers and explore new ideas. The focus is on turning promising approaches into production-ready solutions. It is also not an ML engineering role: you don't need engineering experience, but you should understand what deploying a model involves and the practical constraints around it. We care about approaches that actually work in production, not ones that are too slow or expensive to run, and about the judgement to tell the difference. What you'll bring Deep expertise in mathematical optimization and recommendation algorithms applied to large-scale matching or marketplace problems. Judgement about when to reach for simple statistics, classical ML, LLMs, or agentic approaches. 5+ years applying ML, AI research, optimization, or recommendation systems to complex production problems. Python skills sufficient to build, test, and iterate on working prototypes independently. Able to take a loosely defined product or customer problem and tu

P-1428 At Databricks, we run the world's largest AI platform. We believe the next breakthroughs will come from models trained on domain-specific enterprise data, not from bigger general-purpose models alone. That's why we built AI Runtime , a serverless product that makes GPU training of any size feel simple, so companies can train their own models the way frontier labs do. Customers across industries already use it to train models on governed data, from automotive AI at Rivian to Merck's TEDDY foundation models for single-cell biology and drug discovery, all the way to agents like KARL from Databricks AI Research. It takes a lot of engineering to make that work, and we've written about it on our blog: how we keep GPUs reliable , make training fault-tolerant , and speed up fine-tuning with fused kernels and smart sequence packing. As a Senior Software Engineer, you'll work on the systems behind all of this, solving hard technical problems and helping shape how AI Runtime evolves. The impact you will have: Build core parts of AI Runtime and contribute to Databricks' technical direction in AI training infrastructure. Develop reliability and observability features for a fleet that keeps growing in scale and complexity. Improve AI Runtime's developer experience, so the largest training jobs feel as simple as a first experiment. Help prepare AI Runtime for new GPU generations and workload types. Take on hard multi-node training problems, from orchestration and GPU scheduling to data loading and checkpointing. Make training faster and cheaper by raising GPU efficiency across model architectures and hardware generations. Work with research, product, and customers on what we build next and own features from design through production. Champion engineering excellence, raise the bar, and help the engineers around you grow. What we look for: 5+ years in large-scale distributed systems, with experience in GPU training, high-performance computing, or ML systems. Experience traini

About Outreach Outreach, founded in 2014, is the only complete agentic AI platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few. About the Team The Dialog Understanding and Generation team is building the industry's most advanced agentic AI capabilities for revenue orchestration, the end-to-end coordination and execution of sales and customer engagement workflows. Our mission is to transform raw customer interactions and CRM data into structured intelligence that powers the entire revenue lifecycle. We operate at the intersection of machine learning, LLM-powered systems, and cloud-scale engineering, driving both current and next-generation AI initiatives at Outreach. We are a team of applied data scientists who thrive on solving complex scientific and engineering challenges. The work is roughly an even split between applied ML research and production engineering, so you should be excited about both. You'll collaborate closely with exceptional engineers and product managers in a fast-moving, high-impact environment, applying your creativity and expertise to build innovative solutions that push the boundaries of agentic AI. Your Daily Adventures Will Include: <li aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":

About the position Showpad is building a Revenue Intelligence engine that transforms raw signals—CRM data, email threads, transcripts, and content engagement—into prescriptive AI guidance. We are looking for a technical Product Manager to own the unified data intelligence layer that powers this engine. You will define how we capture and model data, and critically, determine the best retrieval and inference strategies (Search, RAG, Knowledge Graphs, or Vector Similarity) for every use case. Key Responsibilities Data Modeling & Architecture : Define the canonical model for entities (deals, contacts, skills) and establish the retrieval strategy—choosing between keyword, dense vector, hybrid, or graph traversal based on latency and accuracy. Search & RAG Ownership : Own the end-to-end RAG pipeline (chunking, embeddings, indexing). Write technical specs for retrieval layers in Meeting Prep and Roleplay AI, and define evaluation metrics (Recall@k, MRR). Revenue Intelligence Roadmap : Drive the vision from "trusted foundation" to "prescriptive AI." Own the product logic for Deal Health and Winning Behavior models. Platform Enablement : Act as the internal PM champion for the data platform, providing semantic primitives, event taxonomies, and data contracts to other product squads. Discovery & Strategy : Conduct deep customer discovery and stay ahead of LLM/Search trends, translating fuzzy business questions into scoped ML problems. Required Skills Must haves 5+ years in PM: at least 2 years specifically owning data platforms, search, analytics, or ML products. Retrieval Expertise: Hands-on experience shipping RAG pipelines or search systems. You understand the trade-offs between BM25, vector search, and reranking. Data Technicality : Fluency with Datalake architectures (Delta Lake, BigQuery, etc.) and strong entity-relationship modeling skills. ML Fluency : Ability to define features, label designs, and evaluation metrics. You can review a technic