Production Line Management Engineer Jobs

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Join Micron, where your knowledge will influence the future of memory technology! As the Manager for NAND Design Rule/Process Integration, you will lead a talented team to support both development and large-scale production of advanced 3D NAND technologies. This position is essential for producing accurate design-rule and PDK results, ensuring high quality, and maintaining strict documentation across R&D and production efforts. Responsibilities: Lead the release of design rules and PDK deliverables; coordinate DRC waivers, mask definitions, and mask reviews for both R&D and production developments. Own program execution and achievements from kickoff through end-of-life. Partner with collaborators across Array & CMOS Process Integration, Devices, Layout & Build, Modeling, Scribe & Frame, unit process areas (e.g., PHOTO/OPC/CMP), and Quality & Reliability to guide new 3D NAND generations. Ensure high quality and documentation for Build Rule Checks (DRCs) and drive timely disposition of deviations and exceptions. Collaborate with Yield Improvement, Product Engineering, Defect Analysis, and Quality Assurance to identify layout- or database-related process issues and prioritize corrective actions. Build and evaluate te

Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, networking, load balancing (including our public-facing edge and internal service mesh), and observability and alerting systems. The Deployments team designs and maintains our continuous delivery infrastructure, ensuring reliable code deployment from development through production for all engineering teams. This infrastructure is primarily composed of Argo Workflows and ArgoCD. The team also provides tooling that enables clear system ownership and facilitates self-service onboarding for development teams. We are looking to speak to candidates who can work East Coast hours. The ideal candidate should Have 6+ years of experience in software development and operating distributed systems Proficiency in Python, Go, or a similar language Proven experience building and operating large-scale continuous integration and continuous deployment (CI/CD) pipelines Possess a customer-focused mindset Value efficiency in processes and operations Prefer automation over manual process (“allergic to ops work”). We are a small team of software engineers with a strong bias towards software solutions to avoid toil Experience using and extending containerization technologies, particularly Kubernetes, to enhance application agility, optimize resource utilization, and accelerate time-to-market Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure Understanding of Linux operating system internals and networking concepts (e.g., TCP/IP, DNS, TLS, routing) Expectations Contribute to developing a world-class continuous deployment experience, enabling the rapid and reliable shipment of MongoDB products This includes, but is not limited to, contributing to open-source projects, or engineering software-based

Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, networking, load balancing (including our public-facing edge and internal service mesh), and observability and alerting systems. The Deployments team designs and maintains our continuous delivery infrastructure, ensuring reliable code deployment from development through production for all engineering teams. This infrastructure is primarily composed of Argo Workflows and ArgoCD. The team also provides tooling that enables clear system ownership and facilitates self-service onboarding for development teams. We are looking to speak to candidates who can work East Coast hours. The ideal candidate should Have 6+ years of experience in software development and operating distributed systems Proficiency in Python, Go, or a similar language Proven experience building and operating large-scale continuous integration and continuous deployment (CI/CD) pipelines Possess a customer-focused mindset Value efficiency in processes and operations Prefer automation over manual process (“allergic to ops work”). We are a small team of software engineers with a strong bias towards software solutions to avoid toil Experience using and extending containerization technologies, particularly Kubernetes, to enhance application agility, optimize resource utilization, and accelerate time-to-market Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure Understanding of Linux operating system internals and networking concepts (e.g., TCP/IP, DNS, TLS, routing) Expectations Contribute to developing a world-class continuous deployment experience, enabling the rapid and reliable shipment of MongoDB products This includes, but is not limited to, contributing to open-source projects, or engineering software-based

Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, networking, load balancing (including our public-facing edge and internal service mesh), and observability and alerting systems. The Deployments team designs and maintains our continuous delivery infrastructure, ensuring reliable code deployment from development through production for all engineering teams. This infrastructure is primarily composed of Argo Workflows and ArgoCD. The team also provides tooling that enables clear system ownership and facilitates self-service onboarding for development teams. We are looking to speak to candidates who can work East Coast hours. The ideal candidate should Have 6+ years of experience in software development and operating distributed systems Proficiency in Python, Go, or a similar language Proven experience building and operating large-scale continuous integration and continuous deployment (CI/CD) pipelines Possess a customer-focused mindset Value efficiency in processes and operations Prefer automation over manual process (“allergic to ops work”). We are a small team of software engineers with a strong bias towards software solutions to avoid toil Experience using and extending containerization technologies, particularly Kubernetes, to enhance application agility, optimize resource utilization, and accelerate time-to-market Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure Understanding of Linux operating system internals and networking concepts (e.g., TCP/IP, DNS, TLS, routing) Expectations Contribute to developing a world-class continuous deployment experience, enabling the rapid and reliable shipment of MongoDB products This includes, but is not limited to, contributing to open-source projects, or engineering software-based

Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, networking, load balancing (including our public-facing edge and internal service mesh), and observability and alerting systems. The Deployments team designs and maintains our continuous delivery infrastructure, ensuring reliable code deployment from development through production for all engineering teams. This infrastructure is primarily composed of Argo Workflows and ArgoCD. The team also provides tooling that enables clear system ownership and facilitates self-service onboarding for development teams. We are looking to speak to candidates who can work East Coast hours. The ideal candidate should Have 6+ years of experience in software development and operating distributed systems Proficiency in Python, Go, or a similar language Proven experience building and operating large-scale continuous integration and continuous deployment (CI/CD) pipelines Possess a customer-focused mindset Value efficiency in processes and operations Prefer automation over manual process (“allergic to ops work”). We are a small team of software engineers with a strong bias towards software solutions to avoid toil Experience using and extending containerization technologies, particularly Kubernetes, to enhance application agility, optimize resource utilization, and accelerate time-to-market Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure Understanding of Linux operating system internals and networking concepts (e.g., TCP/IP, DNS, TLS, routing) Expectations Contribute to developing a world-class continuous deployment experience, enabling the rapid and reliable shipment of MongoDB products This includes, but is not limited to, contributing to open-source projects, or engineering software-based

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for a People Operations Generalist to join our growing People team. You'll touch the employee lifecycle end-to-end — from offer acceptance through offboarding — while helping to build the processes and documentation that let our People function scale with the business. This is a great fit for a highly organized, systems-oriented people person who thrives in a fast-paced environment and wants to build operational foundations, not just maintain them. What You’ll Do: Own and continuously improve the new hire onboarding experience, ensuring employees are set up for success and internal tasks are tracked and completed on time. Serve as a first point of contact for employee questions across the full HR spectrum, triaging and routing more complex issues to the right People team member or external partner. Maintain and improve self-service resources (FAQs, Notion pages, etc.) so employees ca

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is our evaluation platform — the unified evals backbone that lets teams measure, trace, and trust the quality of LLM and agent systems across the company, powering trace/score ingestion, LLM-as-judge workflows, agent simulations, and LLM observability for the tens of millions of daily requests flowing through our LLM Gateway. We also own core platform surfaces including the Agent Gateway, open-weights model serving and batch inference, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our evals and LLM observability platform: the systems that let teams evaluate, trace, and continuously improve the quality of LLM and agent products. You’ll work across evaluation frameworks and SDKs, OpenTelemetry-based trace/score ingestion, LLM-as-judge and offline/online eval pipelines, agent simulations, data pipelines, backend services, and observability. This role is ideal for an engineer who enjoys building reliable measurement and quality primitives in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and evaluation methodologies are evolving quickly. You’re excited about this opportunity because you will… Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Work on our unified evals platform — evaluation SDKs, OpenTelemetry trace/score ingestion, LLM-as-judge, offline and online eval pipelines, and agent simulations — alongside the LLM Gatew

About the Role Particle41 is seeking a QA Engineer (IC2) to join our engineering team. We’re looking for someone who sees quality as more than testing; someone who takes ownership of the overall customer experience and works closely with engineers and product teams to build reliable, high-quality software. In this role, you’ll be involved from the early stages of development through production release, helping identify risks, refine acceptance criteria, shape test strategy, and ensure features are thoroughly validated before and after deployment. You’ll play an active role in improving team quality practices, raising concerns proactively, and using modern AI-assisted testing approaches to improve efficiency and coverage. If you are passionate about building quality into software from the start -not just validating it at the end; we want to hear from you. Responsibilities Test Planning and Strategy Engage with requirements and acceptance criteria before development begins, identifying gaps, ambiguities, and edge cases early. Contribute to design and refinement discussions to flag testability and quality risks while they are still cheap to fix. Collaborate with stakeholders to define test objectives, scope, and approach aligned to project milestones. Test Automation Write and maintain automated test suites spanning UI, API, contract, and integration layers using TypeScript and Puppeteer (or equivalent tools). Contribute to framework improvements and scaling of automation coverage across the feature area. AI-Augmented Testing Use AI tooling consistently for test design, test data generation, edge-case identification, and debugging and critically evaluate AI outputs before incorporating them. Stay current with evolving AI testing tools and advocate for their adoption where they improve coverage or efficiency. Adopt AI for test design, data generation, and debugging with guidance and critically evaluate AI outputs before incorporating them where you're building the found

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Summary Department Overview The Fab Engineering team supports semiconductor manufacturing by driving process performance, product quality, and operational efficiency. The team partners closely with equipment, quality, and production organizations to enable stable processes, improve yield, and deliver consistent manufacturing outcomes. Join us and make a change! Position Overview As a Fab Engineer, you will own and sustain semiconductor manufacturing processes within the fab. You will solve process issues, analyze performance data, and implement solutions that improve yield, quality, and cycle time. This role plays a direct part in enabling high-volume manufacturing through structured problem solving and multi-functional execution. Responsibilities Own process performance and drive improvements in yield, quality, and cycle time Troubleshoot manufacturing issues using structured methodologies (e.g., root cause analysis, FMEA, SPC, 8D) Analyze process data to identify trends, gaps, and improvement opportunities Define, monitor, and adjust process parameters across semiconductor manufacturing equipment Partner with cross-functional teams to resolve process excursions and implement corrective actions Support qualification and integration of new equipment, materials, and process changes Drive initiatives focused on yield improvement, cost reduction, and productivity Perform defect analysis and long-term investi

Assistant Manager encompasses overseeing all aspects of mining operations, from planning and execution to resource management and quality control. Responsible for ensuring adherence to approved plans and safety protocols, the Manager directs drilling, blasting, and coal seam removal activities, guiding shift leaders to meet production targets. Collaborating closely with colliery engineers, they facilitate continuous improvement initiatives and preparatory works, while also managing equipment maintenance and contractor relations. The Manager plays a pivotal role in driving productivity, cost reduction, and safety enhancement measures, ensuring compliance with regulatory guidelines and DGMS standards. Source: Adani Group | Job ID: 53127

About the Team: OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. Role Overview We are seeking a Package Reliability Engineer to lead reliability engineering for advanced packages used in high-performance AI and computing systems. The primary focus of this role is to assess package level mechanical and thermal reliability risks and apply thermal and mechanical modeling to optimize package design, material selection, and assembly processes. The engineer will also develop reliability test plans with external partners, identify failure mechanisms, perform root-cause analysis, and recommend practical corrective actions. In this role, you will assess package reliability risks from early architecture development through product qualification and high-volume manufacturing. You will work closely with package design, silicon design, system engineering, manufacturing, and ASIC partners to predict package behavior, develop qualification strategies, resolve reliability issues, and improve overall package robustness and lifetime. In this role you will: Lead reliability test plan and assessments for advanced HPC packages, including risk identification, potential failure-mechanism analysis, root-cause investigation, mitigation planning, and corrective-action development. Drive reliability-focused package design optimization based on thermo-mechanical modeling to improve package reliability, power integrity, thermal performance, mechanical robustness, and platform scalability. Develop, validate, and apply package reliability models and lifetime-prediction

Job Title: Principal Software Engineer Location: South San Francisco, CA preferred; Remote optional Job Type: Full-time About the Role We're seeking a Principal Software Engineer to join Natera's R&D team. We build the cloud infrastructure, workflow platforms, and data platforms that R&D uses to do its science - including a Kubernetes-based workflow execution platform, a custom R&D LIMS system, and a set of supporting tools that routinely handle production-scale workloads. This is a senior technical role. You'll own the full architecture, implementation, and delivery of large projects across our platform. You'll partner with the team lead on roadmap and tough technical calls, mentor mid-level engineers, and help the team find the right solution for the problem - not the most elaborate one. We're a small team (4 engineers + lead) with broad scope, and we're heavily AI-augmented: every engineer uses Claude Code / Cursor as a daily tool, and we build our own AI tooling (custom plugins, MCP servers, agents) to multiply ourselves. Responsibilities Architecture & Delivery: Own the design, implementation, and delivery of major initiatives across our platform. Take fuzzy problems through to shipped, working software used in production. Technical Leadership: Lead architecture and design reviews. Help the team find the right solution for the problem and avoid unnecessary complexity. Influence technical decisions across the team. Mentorship: Mentor mid-level engineers through code review, design docs, and pairing. Raise the engineering bar by example. AI-Native Development: Build and improve our AI-coding tooling alongside the rest of the team. Help us push the frontier of agent-driven software development inside the company. Cross-Functional Partnership: Work with bioinformaticians, scientists, and other R&D engineers to understand their needs and translate them into platform capabilities. Partner with the team lead on roadmap, prioritization, and tough te

Zoox is looking for an experienced software engineer to work on large-scale simulation pipelines used to validate the behavior of the Zoox self-driving vehicle. These are data and GPU intensive workloads built on Ray.io and Kubernetes. Given the massive scale and criticality of these pipelines, ensuring their reliability and efficiency has a significant impact on the company's ability to safely and quickly iterate on autonomy development. We are a small, scrappy team within the larger Autonomy organization. Although this role primarily involves off-vehicle pipelines, you will work closely with engineers developing the on-vehicle algorithms and models in our autonomy stack. We stay close to the end users - autonomy engineers - and think about the end to end use case for these validation pipelines. This is a hands-on role with a high degree of independence and ownership. You will be expected to contribute towards the framework’s architecture, reliability, efficiency, and grow its capabilities to support new use cases. You should have a track record of keeping production systems running with high availability. Experience with robotics or autonomous systems is not required but an understanding of the robotic data lifecycle is preferred. In this role, you will: Improve the cost efficiency, reliability, and performance of our validation and simulation pipelines Create production-grade APIs, SDKs, and tools to enable a varied set of validations of autonomous behaviors Improve the ML training pipelines supporting the autonomous behavior org Qualifications Bachelor’s degree in Computer Science or related field and 8+ years of industry experience Experience optimizing large-scale distributed systems for cost and efficiency Experience with AWS or similar providers Proficiency with Python and familiarity with C++ Bonus Qu

Opportunity Overview: We are seeking a Lead Data Engineer to drive the design and delivery of our cloud-native healthcare data platform. You will lead the implementation of scalable data solutions built on AWS, Apache Iceberg, Lake Formation, Glue Catalog, Athena, dbt, and modern orchestration frameworks. This role combines deep hands-on engineering with technical leadership and collaboration across platform, analytics, and business teams. What you’ll do: Lead Data Engineering Initiatives Lead delivery of complex data engineering projects across multiple teams Drive technical execution from design through production deployment Establish scalable implementation patterns Build and Optimize Data Platforms Design and implement batch and near-real-time pipelines Build reusable ingestion, transformation, validation, and publishing frameworks Support modernization of legacy workloads Lakehouse Engineering Lead Apache Iceberg implementation and optimization Define standards for schema evolution, partitioning, compaction, and metadata management Ensure efficient storage and query performance Data Quality and Reliability Implement data quality frameworks Drive observability and monitoring practices Improve operational excellence and reliability Technical Leadership Review architecture and design proposals Conduct code reviews and engineering reviews Mentor engineers and establish best practices ISMS roles and responsibilities: Good knowledge of Information practices. Assist the manager in all the information security activities implementation and maintenance process. Ensuring the team and imparted with Competence related to Information security Responsible for implementation of security policies and procedures and report any issues to the Information Security Manager. Required Qualifications: 8–12 years of Data Engineering experience. Experience leading enterprise-scale data initia

About the Team The Cooperative AI team is scaling to devices and embedded operations and user experiences. Our model-powered scaled workforce and knowledge system are moving on to the edge and powering our devices and edge experiences. By leveraging OpenAI’s state-of-the-art models and technologies, in production and in the lab, we develop systems that reason and work autonomously with customers and with our workforce responsible for operational work. We carry real workloads for critical systems across finance, sales, customer support, integrity, product insights, internal operations, and now devices 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 Technical Lead Manager to lead a team of engineers building AI-native embedded experiences and operations-forward systems. In this role, you will perform both hands-on technical leadership and small team management. You will drive business outcomes, architecture and technical strategy for complex systems, contribute directly to implementation, and help grow a high-performing team. You will work closely with internal stakeholders to understand operational challenges, identify high-leverage opportunities for automation, and deliver solutions that create measurable impact. This role is ideal for someone who enjoys moving between technical design, coding, mentoring engineers, and working directly with users t