ml infrastructure engineer

Youtube

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience.<br>
</li>
<li>8 years of experience in software development.<br>
</li>
<li>5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.</li>
<li>5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.<br>
</li>
<li>5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Master&rsquo;s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field.</li>
<li>8 years of experience in data structures and algorithms, with experience building distributed, large-scale machine learning systems.</li>
<li>3 years of experience in a technical leadership role, defining technical goals, setting roadmap direction, and mentoring executive engineering teams.</li>
<li>Experience designing and deploying recommendation systems, personalized ranking models, or retrieval systems.</li>
<li>Expertise in managing the cold-start problem, reinforcement learning, or exploration/exploitation algorithms.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.</p>
<p>
In this role, you will lead the Short-form Video Exploration team and drive the success of YouTube shorts, a top strategic initiative. The Short-form Video team manages the entire ecosystem: from mobile creation tools and effects (text, stickers, filters) to a seamless, full-screen player experience. We also build the backend systems required to create, serve, and recommend these videos across Home, Search, and beyond.<br>
<br>
Shorts Exploration is a critical component of our recommendation strategy. Our mission is to identify initial audiences for fresh content, expanding our recommendable corpus and inspiring further creation. To achieve this, we leverage advanced modeling techniques and signals to build these audiences directly within the recommendation stack.</p>
At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun &mdash; and we do it all together.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
<br>
<br>
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits<br>
<br>
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Design, develop, test, deploy, maintain, and enhance large scale software solutions.<br>
</li>
<li>Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.<br>
</li>
<li>Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.<br>
</li>
<li>Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.<br>
</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Tech Lead, YouTube Shorts Discovery, ML Recommendations

Google

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience.</li>
<li>2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.<br>
</li>
<li>1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.<br>
</li>
<li>1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>2 years of experience with data structures and algorithms.</li>
<li>Experience developing accessible technologies.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.</p>
<p>
Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.
<br>
<br>
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field - we publish regularly in academic journals, release projects as open source, and apply research to Google products.</p>
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
<br>
<br>
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits<br>
<br>
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Write product or system development code.</li>
<li>Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).</li>
<li>Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.</li>
<li>Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.</li>
<li>Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Software Engineer, AI/ML, Google Research

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience.<br>
</li>
<li>2 years of experience with software development in C++ and Python, or 1 year of experience with an advanced degree.</li>
<li>1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.<br>
</li>
<li>1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Master's degree or PhD in Computer Science or a related technical field.</li>
<li>2 years of experience with data structures and algorithms.</li>
<li>Experience in data analysis for building and optimizing machine learning models, such as neural networks and recommender systems, specifically for large-scale Ads platforms.</li>
<li>Experience developing accessible technologies.</li>
<li>Excellent statistical, quantitative modeling and forecasting skills with experience of working with large and complex data sets.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.</p>
<p>
Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We&rsquo;re made up of multiple teams, building Google&rsquo;s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
</p>Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
<br>
<br>
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits<br>
<br>
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Write product or system development code.<br>
</li>
<li>Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).<br>
</li>
<li>Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.<br>
</li>
<li>Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.<br>
</li>
<li>Implement solutions in one or more specialized Machine Learning (ML) areas, utilize ML infrastructure, and contribute to model optimization and data processing.<br>
</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Software Engineer III, AI/ML, Display Ads

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience.<br>
</li>
<li>5 years of experience with software development in one or more programming languages.</li>
<li>3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization , data processing, debugging).</li>
<li>Experience with core GenAI concepts (LLM, Multi-Modal, Large Vision Models) and experience with text, image, video, or audio generation.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Master's degree or PhD in Computer Science or related technical field.<br>
</li>
<li>5 years of experience with data structures and algorithms.</li>
<li>1 year of experience in a technical leadership role.</li>
<li>Experience developing accessible technologies.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. Google Cloud accelerates every organization&rsquo;s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google&rsquo;s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
<br>
<br>
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits<br>
<br>
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Write product or system development code.</li>
<li>Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).<br>
</li>
<li>Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.<br>
</li>
<li>Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.</li>
<li>Implement GenAI solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Senior AI Software Engineer, Gemini Enterprise, Agent Designer

Occam Solutions

<div><p>Primary Responsibility</p><p></p><p><br>
The <b>DevSecOps Engineer</b> shall serve as the lead technical engineer responsible for designing, implementing, securing, automating, and sustaining the cloud infrastructure supporting the Office of Naval Research (ONR) Data &amp; Analytics environment. The DevSecOps Engineer shall lead the implementation and execution of the technical requirements identified in <b>PWS Section 4.5 &ndash; Technical Infrastructure and Platform Support</b>, including infrastructure automation, Continuous Integration/Continuous Deployment (CI/CD), cloud security, container orchestration, Infrastructure-as-Code (IaC), and operational sustainment of the FedRAMP High and DoD Impact Level 5 (IL5) cloud environment.</p><p></p><p><br>
The DevSecOps Engineer shall architect and maintain secure cloud infrastructure, automate deployment pipelines, support AI/ML platform integration, implement cloud-native security controls, monitor system health and security events, and ensure compliance with DoD cybersecurity requirements. This position will work closely with software developers, data engineers, cloud architects, cybersecurity personnel, and Government stakeholders to deliver resilient, scalable, and secure cloud services.</p><p></p><p><br>
Duties and Responsibilities</p>
<p>
The DevSecOps Engineer shall:</p>
<ul>
<li>Design, deploy, automate, and maintain secure cloud infrastructure supporting mission applications and analytics platforms.</li>
<li>Develop, implement, and manage automated CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, or equivalent technologies.</li>
<li>Build and maintain Infrastructure-as-Code (IaC) solutions utilizing Terraform, Ansible, or similar automation frameworks.</li>
<li>Implement and manage containerized application environments using Docker, Kubernetes, or comparable orchestration platforms.</li>
<li>Configure, monitor, and optimize cloud-native security services including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), endpoint protection, and Security Information and Event Management (SIEM) platforms.</li>
<li>Analyze security logs, audit events, vulnerability reports, and SIEM alerts to identify threats, recommend mitigation strategies, and support incident response activities.</li>
<li>Support continuous Authority to Operate (ATO) compliance through configuration management, security patching, vulnerability remediation, and security documentation.</li>
<li>Implement secure API management, identity and access management (IAM), secrets management, encryption, and least-privilege access controls.</li>
<li>Develop automated monitoring, logging, alerting, and operational dashboards to improve system reliability and performance.</li>
<li>Support deployment and operationalization of AI/ML platforms and services, including cloud-native machine learning environments.</li>
<li>Collaborate with software developers, data engineers, and cloud architects to integrate security throughout the software development lifecycle (SDLC).</li>
<li>Ensure cloud infrastructure complies with DoD cybersecurity policies, FedRAMP High requirements, DoD IL5 controls, and applicable security frameworks.</li>
<li>Support system scalability, resilience, disaster recovery, and operational continuity.</li>
<li>Document system architectures, deployment procedures, security configurations, and operational processes.</li></ul><br>
<p></p>
<p>
Minimum Qualifications</p>
<p>
The DevSecOps Engineer shall meet the requirements for the <b>&quot;DevSecOps Engineer &ndash; Intermediate&quot;</b> labor category as defined in the <b>CHESS ITES-3S</b> contract vehicle and possess the following additional qualifications:</p>
<ul>
<li>Seven (7) years of combined experience in DevSecOps, DevOps, SecOps, or MLOps engineering and development.</li>
<li>Five (5) years of hands-on experience designing, securing, implementing, and maintaining cloud environments, preferably within <b>AWS GovCloud</b>, <b>Azure Government</b>, or similarly regulated cloud environments.</li>
<li>Demonstrated experience supporting cloud environments operating under <b>FedRAMP High</b>, <b>DoD Impact Level 5 (IL5)</b>, or comparable federal security requirements.</li>
<li>Verifiable experience on at least two (2) projects designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Jenkins, GitLab CI/CD, Azure DevOps, GitHub Actions, or equivalent technologies.</li>
<li>Demonstrated expertise implementing and managing containerization and orchestration technologies, including Docker, Kubernetes, OpenShift, or similar platforms.</li>
<li>Hands-on experience implementing Infrastructure-as-Code (IaC) using Terraform, Ansible, CloudFormation, or comparable automation tools.</li>
<li>Experience configuring and administering cloud-native security capabilities including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), Security Information and Event Management (SIEM), vulnerability management, and cloud security monitoring solutions.</li>
<li>Verifiable experience analyzing security logs, SIEM data, audit records, and threat intelligence to identify vulnerabilities, investigate security events, and implement corrective actions.</li>
<li>Experience implementing secure Identity and Access Management (IAM), encryption, secrets management, and Zero Trust security principles.</li>
<li>Experience supporting automated monitoring, logging, system performance optimization, and operational sustainment of enterprise cloud platforms.</li>
<li>Strong understanding of DevSecOps best practices, secure software development lifecycle (SSDLC), and cloud security architectures.</li><br>
</ul>
<p>Qualifications</p>
<ul>
<li>Active Secret security clearance (required).</li>
<li>Current DoD 8140 (or legacy DoD 8570) cybersecurity certification applicable to privileged access functions (e.g., Security+, CySA+, CASP+, CISSP, CCSP).</li>
<li>AWS Certified DevOps Engineer &ndash; Professional, AWS Security Specialty, Azure DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), or comparable cloud certifications.</li>
<li>Experience supporting AI/ML environments, including AWS SageMaker, Azure Machine Learning, or similar platforms.</li>
<li>Experience with streaming data technologies such as Apache Kafka, AWS Kinesis, or equivalent event-driven architectures.</li>
<li>Familiarity with NIST RMF, DISA STIGs, FedRAMP, Zero Trust Architecture, and DoD cybersecurity compliance requirements.</li></ul></div>

DevSecOps Engineer

J2B GLOBAL LLC

<div><div><div><div><h3 class="jobSectionHeader"><b>Principal ML Engineer</b></h3>
</div><div>C2CContract
</div></div><div>Chicago, IL
</div><div>Visa: US CitizenH1BTNGreen CardGC-EADH4-EADEAD
</div><div>Role: Principal ML Engineer Location: Dallas, TX - 5 days onsite from day 1 Secondary locations: Chicago, IL and New York City, NY Job Description: Responsibilities: Contribute to Mail Intelligence Mission: Support the development of AI/ML capabilities that enhance user personalization and insights within Mail and the broader client's ecosystem. Innovate and Personalize: Assist in building scalable AI/ML solutions that identify user interests and habits, contributing to personalized user experiences. Technical Leadership: Guide and mentor a team of passionate engineers, fostering a culture of innovation and excellence. Your leadership will ensure the delivery of high-quality, user-centric solutions. Collaborative Development: Work with cross-functional teams to integrate new AI/ML features and improve existing platforms, ensuring smooth user interactions. Data-Driven Insights: Apply machine learning techniques and big data processing to derive insights from mail data, enhancing content and user knowledge. Tool and Workflow Development: Develop AI/ML tools and workflows to manage and utilize large datasets, supporting AI-based infrastructure and analytics. A/B Testing and Analysis: Run regular A/B tests, gather data, and draw conclusions on the impact of your models. Commitment to Excellence: Maintain high engineering standards, focusing on code quality and architectural skills, while delivering optimal user experiences. Feedback and Recovery: Implement feedback loops and recovery paths to handle AI failures gracefully, ensuring user satisfaction. Balance Tradeoffs: Manage cost, quality, and speed tradeoffs in AI/ML solutions to optimize performance. A lot about YOU Educational Background: You have a strong foundation in computer science, data science, or a related field, with a focus on machine learning and AI technologies. Technical Skills: Proficient in programming languages such as Python or Java and machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn. Skilled with AI tools such as Cursor, Copilot, and Claude. AI Skills: Advanced AI skills, including experience with AI development tools and frameworks. Problem-Solving Ability: You possess strong analytical skills and a knack for solving complex problems using data-driven approaches. Collaboration: You are a team player who can work effectively with cross-functional teams, including tech leads and architects, to deliver high-quality solutions. Passion for Innovation: You are enthusiastic about leveraging AI/ML to drive innovation and enhance user experiences across Mail and the broader ecosystem. Attention to Detail: You maintain high standards for code quality and are committed to delivering robust, scalable solutions. Continuous Learning: You are eager to stay updated with the latest advancements in AI/ML and apply them to improve products and services. Qualifications Educational Requirement: Bachelor's degree in Computer Science, Data Science, or a related field; a Master's degree is a plus. Experience: 8+ years of experience in machine learning, data analysis, or a related field. Technical Proficiency: Expertise in Python, Java, or C++ and machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn. Skilled with AI tools such as Cursor, Copilot, and Claude. Data and Infrastructure: Proficient in big data technologies (Hadoop, Spark) and modern ML infrastructure (Airflow, MLFlow, feature stores, vector databases, CI/CD for ML). Generative AI and Optimization: Hands-on experience with LLMs, generative AI (prompt engineering, Agent frameworks, fine-tuning, RAG, LLMOps), and GPU optimizations. Observability and Feedback: Familiar with observability, telemetry, and user feedback loops for product improvement. Algorithm Knowledge: Solid understanding of machine learning algorithms, natural language processing, and data mining techniques. Problem-Solving Skills: Demonstrated ability to solve complex problems and implement efficient, scalable solutions. Collaboration Skills: Ability to work effectively in a team environment and collaborate with tech leads, architects, and cross-functional teams. Communication Skills: Strong verbal and written communication skills to articulate complex technical concepts to non-technical stakeholders. Continuous Improvement: Eagerness to learn and adapt to new technologies and methodologies in the AI/ML domain. Preferred Qualifications Mail Experience: Prior experience working with email systems or related technologies is a plus. Personal Attributes: Self-driven and detail-oriented, with a passion for tackling challenges. Strong teamwork spirit, excellent communication skills, and the ability to multitask and manage expectations effectively. Cloud Experience: Experience with public cloud platforms, especially Google Cloud Platform (GCP), is preferred.</div></div><p></p><div><p> </p><p>Refer</p></div></div>

Principal ML Engineer

Lightning AI

<div><div><h2 class="jobSectionHeader"><b>Who </b><b>We Are</b></h2>
<p>
Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems&mdash;designed to take ideas from research to production with less friction.</p>
<p>
Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.</p>
<p>
We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.</p>
<h2 class="jobSectionHeader"><b>
The Way We Work</b></h2>
<p>
The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:</p>
<ul>
<li><b>Move with Urgency:</b> We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.</li>
<li><b>Take Ownership:</b> We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.</li>
<li><b>Communicate Openly:</b> We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.</li>
<li><b>Build Great Teams:</b> We lead by example, empower others, and create healthy teams where people can do their best work.</li>
<li><b>Raise the Bar:</b> We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.</li>
<li><b>Think Long-Term:</b> We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.</li>
</ul>
</div><div></div><h2 class="jobSectionHeader"><b>What We're Looking For</b></h2>
<p>
Lightning AI is looking to hire an <b>AI</b> <b>Platform Support Engineer </b>to join our US Customer Experience team, supporting ML engineers running large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments.</p>
<p>
This role sits at the intersection of ML systems, cloud infrastructure, Kubernetes, and customers. You'll support engineers training models, deploying inference systems, and scaling GPU workloads in production.You are not a ticket router or traditional support engineer. You are a technical partner to ML teams - helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems.</p>
<p>
The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You'll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications.</p>
<p><i>
This role is hybrid out of our Seattle, San Francisco, or New York office hubs, with an in-office requirement of at least 2 days per week and occasional team and company offsites. The role follows a Monday&ndash;Friday schedule, with working hours from 8:00 AM to 5:00 PM PST. We are not able to provide visa sponsorship for this role at this time.</i></p><h2 class="jobSectionHeader"><br>
<b>
What You'll Do</b></h2>
<p><b>
Work Directly With ML Engineers</b></p>
<ul>
<li>Partner directly with customer engineering teams running training and inference workloads in production</li>
<li>Help customers diagnose and resolve complex distributed systems and ML infrastructure issues</li>
<li>Act as a technical advisor during high impact incidents and platform degradation events</li>
<li>Translate infrastructure level issues into actionable guidance for ML engineers</li>
<li>Build credibility with customers through strong technical reasoning and clear communication</li>
</ul>
<p><b>Debug ML Infrastructure &amp; Distributed Workloads</b></p>
<ul>
<li>Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems</li>
<li>Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues</li>
<li>Analyze logs, metrics, traces, and system behavior to isolate root causes</li>
<li>Debug containerized workloads running across Kubernetes and bare metal GPU environments</li>
<li>Support customers scaling workloads across multi node GPU systems</li>
<li>Diagnose performance bottlenecks involving compute, memory, networking, or storage</li>
</ul>
<p><b>Improve Reliability &amp; Platform Operations</b></p>
<ul>
<li>Identify recurring patterns across customer issues and drive long term reliability improvements</li>
<li>Contribute to post incident reviews and operational improvements</li>
<li>Build internal tooling, automation, documentation, and runbooks</li>
<li>Partner closely with infrastructure, networking, and platform engineering teams</li>
<li>Help improve observability, operational visibility, and troubleshooting workflows</li>
<li>Improve the customer experience through better processes and technical guidance</li>
</ul>
<p><b>What This Role Is Not</b></p>
<p>
To set clear expectations:</p>
<ul>
<li>This is not a traditional help desk or ticket routing support role</li>
<li>This is not purely customer success or account management</li>
<li>This is not a backend engineering role</li>
<li>This is not a passive escalation position</li>
</ul>
<p>This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers.</p><br>
<p></p>
<h2 class="jobSectionHeader"><b>
What You'll Need</b></h2>
<h3 class="jobSectionHeader"><b>Required Qualifications</b></h3>
<h4 class="jobSectionHeader"><b>Infrastructure &amp; Systems</b></h4>
<ul><li>Strong software engineering and systems troubleshooting background</li>
<li>Experience with Kubernetes and containerized environments</li>
<li>Linux systems knowledge, including networking, storage, process management, and performance tuning</li>
<li>Experience with cloud infrastructure and distributed systems</li>
<li>Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry</li>
</ul><h4 class="jobSectionHeader"><b>ML Infrastructure Experience</b></h4>
<ul><li>Hands on experience operating machine learning workloads in production or research environments</li>
<li>Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL</li>
<li>Familiarity with GPU infrastructure and orchestration</li>
<li>Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure</li>
<li>Understanding of the operational challenges involved in running ML systems at scale</li>
</ul><h4 class="jobSectionHeader"><b>Collaboration</b></h4>
<ul><li>Strong communication skills and ability to work directly with highly technical customers and engineering teams</li>
<li>Comfortable operating in fast moving, highly ambiguous environments</li>
<li>Enjoys solving complex technical problems collaboratively</li>
</ul><h3 class="jobSectionHeader"><b>Ideal Experience</b></h3>
<ul><li>Experience with large scale model training or distributed inference systems</li>
<li>Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms</li>
<li>Experience with InfiniBand, RDMA, or high-performance networking</li>
<li>Experience operating bare metal infrastructure</li>
<li>Familiarity with storage systems commonly used in ML environments</li>
<li>Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company</li>
<li>Contributions to platform engineering, developer infrastructure, or operational tooling projects</li>
<li>Experience writing automation, tooling, or scripts in Python or similar languages</li>
</ul><div><h2 class="jobSectionHeader"><b>Benefits and Perks</b></h2>
<p>
We offer a comprehensive and competitive benefits package designed to support our employees' health, well-being, and long-term success:</p>
<ul>
<li><b>Comprehensive Health Coverage:</b> Medical, dental, and vision coverage for employees and eligible dependents.</li>
<li><b>Meaningful Equity:</b> RSUs that give employees a stake in the company's long-term success.</li>
<li><b>Retirement Savings:</b> 401(k) matching (U.S.) and pension contributions (U.K.).</li>
<li><b>Flexible Time Off:</b> Unlimited PTO, company holidays, and floating holidays to support work-life balance.</li>
<li><b>Company-Wide Winter Break:</b> Two weeks of company closure each winter to disconnect and recharge.</li>
<li><b>Paid Parental &amp; Family Leave:</b> Paid leave to support you and your family through life's important moments.</li>
<li><b>Professional Development:</b> Annual learning and development allowance to support your professional growth.</li>
<li><b>Wellness Benefits:</b> Wellness and work-from-home stipends to support your physical and mental well-being.</li>
<li><b>Sabbatical Program:</b> Four weeks of paid sabbatical leave after four years of service.</li>
<li><b>Flexible Work:</b> Flexible schedules and a hybrid work model for our office-based teams.</li>
<li><b>In-Office Meals:</b> Complimentary meals at our office hubs.</li>
</ul>
<p>Benefits may vary by location, team, and role.</p><br>
<p></p>
<p><i>
At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.</i></p></div></div>

AI Platform Support Engineer (US)

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