Production Scheduler Enhancement Engineer in Seattle

About the team OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development. About the role Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production alongside our most strategic customers. You will own discovery, technical scoping, system design, build, and production rollout, partnering directly with customer engineering and domain teams. You will measure success through production adoption, measurable workflow impact, and eval-driven feedback that changes product and model roadmaps. You’ll work closely with our Product, Research, Partnerships, GRC, Security, and GTM teams. This role is based in Seattle. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required. In this role you will Own technical delivery across multiple deployments from first prototype to stable production Build full-stack systems that deliver customer value and sharpen how we learn Embed closely with customer teams, understand their needs, and guide adoption of what you build Scope work, sequence delivery, and remove blockers early Make trade-offs between scope, speed, and quality; adjust plans to protect delivery Contribute directly in the code when progress or clarity depends on it Codify working patterns into tools, playbooks, or building blocks that others can use Share field feedback that helps Research and Product understand where the models succeed and where they can improve Keep teams moving through clarity and follow-through You might thrive in this role if you Bring 5+ years of engineering or technical deployment experience that includes customer-facing work Have scoped and delivered complex systems in fast-moving or ambiguous environments Write and review production-grade code across frontend and backend using Python, JavaScript, or co

About the team OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development. About the role Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production alongside our most strategic customers. You will own discovery, technical scoping, system design, build, and production rollout, partnering directly with customer engineering and domain teams. You will measure success through production adoption, measurable workflow impact, and eval-driven feedback that changes product and model roadmaps. You’ll work closely with our Product, Research, Partnerships, GRC, Security, and GTM teams. This role is based in Seattle. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required. In this role you will Own technical delivery across multiple deployments from first prototype to stable production Build full-stack systems that deliver customer value and sharpen how we learn Embed closely with customer teams, understand their needs, and guide adoption of what you build Scope work, sequence delivery, and remove blockers early Make trade-offs between scope, speed, and quality; adjust plans to protect delivery Contribute directly in the code when progress or clarity depends on it Codify working patterns into tools, playbooks, or building blocks that others can use Share field feedback that helps Research and Product understand where the models succeed and where they can improve Keep teams moving through clarity and follow-through You might thrive in this role if you Bring 5+ years of engineering or technical deployment experience that includes customer-facing work Have scoped and delivered complex systems in fast-moving or ambiguous environments Write and review production-grade code across frontend and backend using Python, JavaScript, or co

About the team OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development. About the Role We are looking for customer-focused software engineers to build effective custom software that leverages OpenAI’s APIs to solve real customer problems. As an FDSWE, you will work with our customers and OpenAI Forward Deployed Engineers to design and implement scalable solutions that solve their most difficult problems. You will design abstractions to solve customer problems, and then use them to scale our speed and quality of delivery across all Forward Deployed engagements. You will collaborate closely with Sales, Solutions Engineering, Solutions Architects, and Customer Success Managers who work on the same account. You will also work with our Research and Applied Product and Engineering teams to provide insightful customer feedback. This role is based in Seattle. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required. In this role, you will: Embed deeply with strategic customers to understand their business challenges and technical requirements in detail. Design, architect, and develop full-stack solutions using an experiment-driven, iterative approach. Prepare detailed scopes of work and project plans for both proof-of-concept prototypes and full production deployments. Work hands-on with customers' technical teams as a technical expert and trusted advisor, coding side-by-side to drive projects to completion on their infrastructure. Collaborate with Product, Research and Applied teams to ensure seamless customer experiences, project success and actionable product feedback Contribute to internal knowledge bases, codifying best practices and sharing insights gained from customer engagements to scale the Forward Deployed Engineering function. You’ll thrive in this rol

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe. The team is growing, and expanding into multiple new areas. We are building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for 50+ global payment methods. What you’ll do You will own ML problems from conception to production: analyzing new fraud and abuse patterns, exploring large-scale datasets, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance. Your work may include developing transformer architectures for payment data, building graph-powered intelligence across consumers, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections with frontier AI labs. You will have opportunities to ship 0→1 products, and to improve mission-critical ML models operating at Stripe scale. Responsibilities Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network. Investigate emerging fraud and abuse patterns, identify whe

The Forward Deployed Engineer, Staff (FDE) is a high impact technical leader responsible for translating the immense power of Talkdesk's Agentic AI platform into transformative, production grade solutions for our strategic enterprise customers. You are a unique blend of a highly experienced software engineer, a technical project lead, and a hands-on builder. You own the technical execution for deployments, mitigate technical risks, and drive successful customer outcomes across assigned projects. This role is for the experienced builder who excels in high stakes, customer facing environments and is passionate about defining the future of Customer Experience Automation. Responsibilities Technical Project Leadership & Architecture: Act as the technical lead for complex deployments. Design and implement the architectural blueprint for AI agent solutions, manage cross-system dependencies, and ensure designs meet stringent enterprise standards for security and scale. Hands-On Engineering & Delivery: Write production-grade code and leverage Talkdesk and 3rd-party APIs/SDKs to design, build, test, and deploy AI agents. Drive the execution from prototype through to production deployment, ensuring technical quality. Technical Consultation & Alignment: Serve as a trusted technical expert for our AI solutions. Confidently address deep technical inquiries, mitigate technical risk, and build trust with customer Engineering Directors, and technical architects. Influence Product & Engineering Roadmap: Synthesize and codify deployment learnings into reusable solution patterns and tooling. Provide actionable feedback to core Product and Engineering teams to help inform future product direction. Technical Guidance: Mentor junior FDEs and technical specialists on best practices for complex AI architecture, production quality, and client-facing technical delivery. Who You Are We are looking for an autonomous, results-driven technical leader who thrives at the intersectio

About Us At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. About the Team The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market , shaping how LangChain is adopted in the field and feeding real-world insi

Job ID: 111265 At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Responsibilities: Maintain and document current and new system requirements. Perform Server Administration tasks, including user and group administration, research event logs to continually improve service for end users. Work with product, engineering, and operational business stakeholders to understand and prioritize business system’s needs. Monitor infrastructure and platform performance. Create architectural designs that enhance resilience and promote business continuity. Manage the deployment of new software, version upgrades, patch releases and major incidents. Manage development, test, release, and production environments in Cloud. Identify actionable, value-added optimization and efficiency opportunities, and contribute to the continuous improvement of applications and processes. Manage stakeholder requirements and develop detailed implementation plans. Employ agile methodology in the development of software, systems interfaces, and application configurations. Experience: Education and Experience: Master's degree or foreign equivalent in Computer Science, Engineering, or related field. 2 years of experience in job offered, software development, or related occupation. Skills: Experience with IT systems and Business Systems Oracle Fusion cloud. Experience with Oracle Cloud ERP and Boundary apps. Experience with SQL, PL/SQL, BI, OTBI, and BICC reports. Experience with Oracle finance and Source Code Management (SCM). Benefits: Great medical, dental, and vision insurance options with additional programs available when enrolled Mental health benefits Family building benefits Child care and pet benefits 401(k) plan to help save for your future In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid tim

Adobe Firefly’s Generative AI Services team is seeking a Senior Staff Machine Learning Engineer for our GenAI Services area. In this high-impact role, you will work with a team of talented engineers in building scalable, high-performance generative AI systems—powering features across Adobe products like Firefly, Photoshop, Illustrator, Express, Stock, and Premiere. You will design and develop efficient inference pipelines, optimize models for latency and through at inference, and build APIs and ecosystems that integrate both Adobe’s first-party and third-party generative models into Adobe suite of products that serve individual and enterprise customers. You will tackle Adobe’s most complex engineering challenges at the forefront of the the industry, set technical direction, and mentor other ML engineers. Job Responsibilities Design and Development of core GenAI services and APIs that integrate a wide range of generative models into Adobe’s flagship products. Design and build ML workflows for enterprise-scale model customization, serving, and ecosystem integration. Collaborate with Adobe Research and other model developer teams with a focus on model inference strategies and productization of those model Build and optimize GPU-accelerated pipelines for both (customized) model training and inference—prioritizing performance, scalability, and reliability. Foster a culture of innovation , technical excellence, and continuous improvement across the organization. What You’ll Need to Succeed MS or PhD in Computer Science, Machine Learning, or a related field—or equivalent industry experience. 10+ years of experience in machine learning, including production-scale deployments. 3+ years of experience leading large-scale, GPU-intensive GenAI systems (training,

Who we are About Stripe Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. What you’ll do Responsibilities Design, build, and operate critical tier-0 backend services that must achieve 99.99%+ availability and handle security-sensitive data. Responsible for improving developer tooling. Design and implement robust, scalable infrastructure, participate in an on-call rotation, and collaborate with engineering teams across the company to plan and safely execute changes in production. Set up and operate infrastructure that participates in decentralized distributed systems, including creating deploy pipelines for artifacts. Build and harden custom Amazon Machine Images (AMIs) used to bootstrap EC2 instances, and implement monitoring, logging, and alerting for these systems. Participate in roadmap planning and prioritization. Deliver value through a strong collaborative approach with multiple customers and stakeholders across Stripe. Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science or a related field, plus three (3) years of software engineering or related experience. Must have three (3) years of experience in each of the following: Cloud technologies, including AWS and Azure; Go, Python or Java; Developing, maintaining and debugging distributed systems; Linux OS; Metrics, alarms and dashboards; NoSQL databases; and Handling critical incidents for production systems. Salary: $156,800 - $235,200/yr This salary range represents the base s

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Responsibilities: Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context. Perform exploratory data analysis to gain a deeper understanding of the problem. Construct and fit statistical, machine learning, or optimization models. Write production modeling code. Collaborate with Software Engineers to implement algorithms in production. Design and run both simulated and live traffic experiments. Analyze experimental and observational data. Communicate findings. Facilitate launch decisions. Experience: Master’s degree or foreign equivalent in Mathematics, Statistics, Economics, or related field. 2 years of experience in job offered, Operations Research, or related occupation. Experience with solving unstructured and non-standard mathematical problems. Experience with end-to-end data including querying, aggregation, analysis, and visualization. Experience with Python and other programming languages such as R and MATLAB. Benefits: Great medical, dental, and vision insurance options with additional programs available when enrolled Mental health benefits Family building benefits Child care and pet benefits 401(k) plan to help save for your future In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible Subsidized commuter benefits Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation

About the Team The Cooperative AI team is scaling OpenAI with OpenAI. We are building a model-powered scaled automated workforce and knowledge system that evolves and learns alongside a human workforce. By leveraging OpenAI’s state-of-the-art models and technologies, some already in production, others still in the lab, we develop systems that reason and work autonomously for a wide variety of operational work. We leverage real workloads for critical systems across finance, sales, customer support, integrity, product insights, internal operations, and more in order to drive insights into product and industry. We partner closely with internal teams and external customers globally, operating in a hyper-fast feedback loop where many of our users are just a few steps away. This proximity allows us to iterate quickly, validate impact in real time, and accelerate industry impacting learnings and systems builds. We are a highly multidisciplinary, self-contained team focused on transforming the workplace via smart systems, knowledge, scalable and reliable primitives that apply world-class AI capabilities across domains. Our mission is to learn fast and transform how humans collaborate with AI at scale. About the Role We are looking for a hands-on Engineering Manager to lead a small, fast-moving team building AI-powered automation systems that redefine how work gets done across OpenAI. This role sits at the intersection of applied AI, research, and product engineering. You’ll lead a team that builds systems that know how to learn from humans, and carry real workloads across, sales, support, finance, IT, and more, while staying deeply involved in the technical work. You will operate in a highly iterative environment, deploying systems directly to internal users, gathering rapid feedback, and evolving solutions in real time. This is a high-ownership role for someone excited about building 0→1 systems, working closely with customers, and shaping how AI transforms operational wor

About the Team The Statsig team within OpenAI owns the experimentation, rollout, dynamic configuration, and analytics infrastructure that sits on the launch path for OpenAI products. Our systems help teams ship safely, evaluate product and model changes in production, and make high-confidence decisions from real-world usage. Statsig began as an independent company built around experimentation, feature management, and product analytics at scale. After Statsig joined OpenAI, the team began the next chapter: bringing that platform expertise and infrastructure into OpenAI as the experimentation and rollout foundation for every product we ship. This is infrastructure with a very direct product consequence. Teams working on ChatGPT, Codex, model measurement, consumer experiences including ads, business subscriptions, developer products, and shared platform systems depend on Statsig to evaluate configurations, move traffic safely, ingest experiment data, serve analytics, and roll changes forward or back when production reality demands it. We are at a critical point in the platform journey. Adoption is accelerating quickly across OpenAI, and the systems that were already important are becoming load-bearing for how the company launches. The infrastructure needs to stay fast under sharply increasing evaluation volume, reliable when more services depend on it, observable enough to debug quickly, and efficient enough to support OpenAI-wide scale. Recent SDK and server-side infrastructure work has already produced measurable wins in latency, reliability, memory usage, and compute efficiency across important services. The next phase is to make those gains systematic: a platform that can absorb rapidly growing product velocity while preserving low latency, data quality, operational safety, and developer trust. Based out of OpenAI's Bellevue office, we are a close-knit team that values in-person collaboration, technical depth, operational ownership, and building infrastructure that

RDQ427R130 At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world’s best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. More about the team: A great platform needs an exceptional developer experience. Databricks Repos is the source control and workflow foundation that powers how data engineers, ML engineers, and analysts manage code, collaborate with teammates, and ship data and AI applications to production. The Repos team builds the version control experience inside Databricks and defines how users connect their Git providers, manage code changes, track lineage, review pull requests, and promote code into jobs, pipelines, and deployments. We are responsible for creating a simple, reliable, AI native workflow that blends the best of Git style development with the unique needs of data and AI teams. Our mission is to provide a smooth authoring to production journey across notebooks, SQL, Python, and Lakeflow, all in a unified platform. You will drive the vision and roadmap for Databricks Repos and define how developers build, test, review, and ship code inside Databricks. You will work across Workflows, Notebooks, Lakeflow, Data Management, and the broader Developer Experience teams to deliver an integrated and intuitive path to production. The impact you will have: You will design the end to end path to production experience for data and AI teams, from authoring to Git integration to CI and CD. You will define how Repos integr

P-1413 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. We are the UI Platform team, responsible for enabling all frontend developers at Databricks to build amazing features. We support hundreds of developers working in the frontend every week, making sure that they can use AI tooling to build features, can quickly ship them to production, and have best-in class monitoring and observability of what they build.. We own all aspects of the dev loop, relying on industry-standard technology like Typescript, React, GraphQL, yarn, node and more! We're seeking a dedicated Engineering Leader for our Developer Experience team. This team is responsible for the entire life cycle of a feature: dev loop, submitting, pushing, monitoring… everything needed to ensure smooth delivery of code from the developer’s head to production! You will report directly to the Senior Manager of Engineering. The main responsibilities include: You will lead a talented engineering team in UI Platform focused on making our frontend developer experience amazing. You will oversee sustained recruitment of top-tier talent, and developing talent on the team. You will build processes to implement product vision and strategy, according to organizational goals and priorities. You will build software that is not just high quality, but easy to operate. You will manage technical debt, including long-term technical architecture decisions, and balance product roadmap. What we look for: 5+ years of hands-on experience in web frontend development, with a strong preference for React-based applications 2+ years leading and managing a high-performing engineering team. A passion

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, norma