Lead Codex Deployment Engineer Jobs

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Senior Software Engineer on our Geometry team, you will drive foundational algorithms for real-time 3D content creation and editing that power the Roblox platform. Working in a small, autonomous team, you will solve novel math computational problems and deliver immersive, expressive creative tools to millions of users. At this level, you are expected to own pod-level or team-level projects from inception to implementation, provide technical direction to junior collaborators, and contribute to the team’s long-term technical strategy. You Will: Own End-to-End Development: Lead complex, team-level projects through the full development lifecycle, from research and prototyping to production deployment and maintenance. Architect Robust Solutions: Develop efficient algorithms for geometry processing (e.g., convex decomposition, mesh partitioning, boolean operations) that support real-time simulation, collision detection, and performance requirements across all platforms. Mentor and Lead: Provide technical guidance and mentorship to junior engineers, fostering a culture of high standards, code quality, and collaborative architectural discussions. Solve Computational Problems: Tackle discrete m

Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you’ll be part of a passionate team dedicated to accomplishing hard things, together. Location- Chennai Team: Engineering Enablement Group As a Senior Software Engineer in our Engineering Enablement Group, you will lead the re-design and evolution of our Mobile Branding framework — the system that enables customers to create custom-branded versions of the Appian mobile application for both iOS and Android. You will drive the architectural modernization of the end-to-end branding pipeline, from the customer-facing Forum application and provisioning tools to the backend build service running on Mac EC2 runners in AWS. By leveraging modern microservices, CI/CD automation, and cloud-native infrastructure, you will transform the current system into a more reliable, scalable, and maintainable platform that reduces manual intervention and accelerates customer delivery. We are looking for a technical leader who can bridge the gap between complex Ruby/Bash-based tooling, Appian process models, and AWS infrastructure to deliver a seamless mobile branding experience. Primary Qualifications: 6-9 Strong working experience with Android and iOS frameworks and mobile application development workflows. Familiarity with mobile build systems (Fastlane, Xcode, Gradle) and code-signing workflows. Experience with proficiency in Python, with experience in Ruby, Bash, or Go being a plus. Advanced experience with AWS infrastructure (S3, Lambda, EC2) and CI/CD pipeline design. Strong end-to-end knowledge of pipeline creation, deployment automation, and infrastructure-as-code (Terraform). Familiarity with monitoring, observability, and performanc

Backend Engineer (Senior Level) - SDE IV We're looking for a Senior Backend Engineer to lead the architecture and evolution of backend services that deploy and serve machine learning models in production. You'll work closely with ML Engineers, Platform, and Product teams to build scalable, reliable systems and drive technical direction across multiple teams. What You’ll Do Design and drive the long-term architecture of backend services for biometrics and ML model serving. Collaborate with core platform and backend teams on organization-wide architectural initiatives. Partner with business and engineering teams to design and deliver cross-cutting platform capabilities. Lead architectural reviews, mentor engineers, and promote engineering best practices. Build and maintain backend services for deploying and serving ML models Monitor service reliability, performance, and scalability in production Deploy and operate services on AWS using ECS + Fargate, SageMaker, or EC2 + Kubernetes Support real-time and batch inference workflows Contribute to CI/CD pipelines and deployment automation What We’re Looking For Strong expertise in backend development using Java and working knowledge of Python. Experience mentoring engineers and driving architectural decisions. Working knowledge of Python, especially for ML-related workflows Hands-on experience with AWS (e.g., DynamoDB, ECS, EC2, Redis, S3, SageMaker) Familiarity with Terraform or other infrastructure-as-code tools, and experience with CI/CD and production monitoring Experience with observability tools (Datadog, New Relic, etc.) Experience with containers and orchestration (Docker, ECS, etc.) Understanding of how ML models are deployed and served in production Experience with Kubernetes Nice to Have Experience with MLOps or ML platform engineering. Experience with asynchronous programming and event-driven systems. Jumio Values: IDEAL: Integrity, Diversity, Empowerment, Accountability, Leading Innovation Equal Opportunities :

Role Purpose We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on ex

About the Role Amplitude's Cloud Platform team builds the systems that every Amplitude engineer relies on every day to ship code — and we're rebuilding them for the AI era. As a Senior Platform Engineer, you'll own medium-to-high-complexity platform projects end-to-end and help shape a platform where AI agents are first-class users alongside humans: kicking off deploys, opening pull requests against infrastructure, and triaging incidents, so a single engineer can get the throughput of a team. You'll partner with Staff engineers and product teams to make Kubernetes effortless across the engineering org, building self-service automation and scalable AWS infrastructure that lets product teams ship faster, safer, and with less cognitive load. If you're excited about building the systems that other engineers will rely on every day, this role is for you. Key Responsibilities Lead high-impact platform projects — design and ship capabilities that move the needle on developer experience, reliability, or security, and set the bar for quality, testing, and safe deployment practices. Build the AI-augmented platform. Design tooling and workflows that help engineers get more out of AI-assisted development — think infra primitives that are easy to reason about, automated review, and policy-as-code that keeps the guardrails strong as AI shifts how code gets written. Own Infrastructure-as-Code for Kubernetes, AWS, and GCP using Terraform, Helm, Kustomize, and emerging tooling — and make it consumable enough that an LLM can safely PR against it. Evolve our CI/CD backbone (Argo CD / Workflows / Rollouts, GitHub Actions) to make deploys faster, safer, and easier to reason about. Instrument and operate. Drive observability with Datadog and Amplitude, own dashboards and SLOs, and use the data to push reliability forward. Participate in on-call, lead incident response when needed, and turn postmortems into durable platform improvements. Reduce toil and tech debt with pragmatic remediation

Machine Learning Engineer IV – (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Strong industry experience in Machine Learning, dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on exper

Machine Learning Engineer We’re looking for a Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: Multiple years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native:

Citibank, N.A. seeks an Applications Development Tech Lead Analyst for its Tampa, Florida location. Duties: Responsible for source code review to ensure it satisfies good coding practice. Coordinate all communication between tech team and Ab Initio vendor, including scheduling and chairing weekly meetings. Responsible for reconfiguration and reengineering to optimize performance. Design, develop and modify software systems, using scientific analysis and mathematical models to predict and measure outcomes and consequences of design. Prepare reports or correspondence concerning project specifications, activities, or status. Confer with systems analysts, engineers, programmers and others to design systems and to obtain information on project limitations and capabilities, performance requirements and interfaces. Manage the hardware infrastructure and monitor the performance metrices E2E. Identify areas of growth need and proposal scale up activities. Execute on scale up activities by submitting procurement requests, manage regular calls with the deployment team and plan go live/switch activity. Develop shell scripting code base to support the job executions, interactions between systems. A telecommuting/hybrid work schedule may be permitted within a commutable distance from the worksite, in accordance with Citi policies and protocols. Requirements: Requires a Bachelor’s degree, or foreign equivalent in Information Technology, Engineering (any) or related field and 6 years of progressively responsible, post-baccalaureate experience as a Software Engineer, Associate Director – Data Engineering, Senior Consultant, Data Specialist, Assistant Systems Engineer or related position involving gathering new business requirements, architecture, analysis, estimation, design, implementation, and leading a team of software developers and software development. 6 years of experience must include: Experience with AbInitio Data Processing (SME), Data Modeling

About the team OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We embed deeply with users to solve high-leverage problems. We move quickly from prototype to deployment and surface patterns that shape the platform. We operate at the intersection of customer delivery and core development. We work closely with Product, Research, and Go-To-Market (GTM). About the role Forward Deployed Engineers lead complex deployments of frontier models in production. You will embed with customers where model performance matters, delivery is urgent, and ambiguity is the default. You will use this to map their problems,You will use this to map their problems, structure delivery, and ship fast. You will scope, sequence, and build full-stack solutions that create measurable value. You will also drive clarity across internal and external teams. You will identify reusable patterns and share field signal that influences the roadmap. Success in this role means owning the delivery state across workstreams. You will hold the bar on quality and pace and help OpenAI learn through execution. This role is based in Abu Dhabi. 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 c

We are seeking a highly motivated and experienced Lead Java Developer to join our Credit Risk Technology team. This is a hands-on leadership role where you will be instrumental in designing, developing, and deploying robust, high-performance, and large-scale software solutions for Counterparty Credit Risk Management. You will be at the forefront of firm-wide initiatives, leveraging best-in-class software stacks and cutting-edge AI tools to solve complex challenges. You will lead a team of talented engineers, providing technical guidance, fostering innovation, and ensuring the delivery of high-quality, scalable, and resilient software. The ideal candidate is passionate about modern software development practices, proficient with contemporary tech stacks, and eager to utilize artificial intelligence to manage Citi’s exposure to financial institutions, governments, and corporates. This role offers the opportunity to make a significant impact by contributing to critical systems that provide an integrated view of trades, collateral, and market data from numerous sources. Key Responsibilities: Technical Leadership & Hands-On Development: Lead by example, actively contributing to the design, architecture, and hands-on development of critical software components for Counterparty Credit Risk Management leveraging AI tools such as Devin and Co-Pilot Drive technical excellence, ensuring best practices in coding, testing, and deployment are followed for high-performance and large-scale applications. Conduct code reviews, provide constructive feedback, and mentor team members in advanced development techniques. Team Leadership & Management: Supervise work of a team of software engineers Foster a collaborative, innovative, and inclusive team environment focused on tackling challenging firm-wide initiatives. Allocate resources effectively to meet project deadlines an

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 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 New York. 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 c

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 San Francisco. 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,

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

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. Position Overview: We are seeking a highly technical Staff Observability Site Reliability Engineer with a specialty in Splunk to own and evolve our Splunk ecosystem. In this role, you will move beyond simple monitoring to delivering a world class, comprehensive, scalable Observability Platform that enables our SRE teams and business partners. You will treat infrastructure as code —utilizing Terraform and strong coding proficiency in Go, Python, or Ruby —to automate the deployment of agents and collectors across complex distributed systems. Key Responsibilities Automated Infrastructure: Design, build, and maintain scalable observability infrastructure using tools like Terraform. Splunk Engineering: Optimize the collection, processing, and storage of log data to ensure high reliability and low latency of our Splunk services Incident Response: Participate in on-call rotations and lead post-incident reviews to drive systemic improvements and "observability-driven development." Automation: Eliminate "toil" by automating the deployment and scaling of observability agents and collectors. Required Skills & Experience (The Essentials) Log Management: Minimum 5+ Experience scaling and managing Splunk Cloud at scale (1000+ SVCs), including Workload Management (WLM) and HEC optimization. Visualization: Expertise in creating intuitive, actionable Splunk dashboards that correlate data across multiple sources. SRE Mindset: Minimum 5+ years of experience in an SRE, Dev