Production Tech Jobs

As organizations rapidly adopt AI applications and agentic systems, security teams need visibility and control over how these technologies are being used. Datadog's AI & Data Security product helps customers discover, secure, and govern AI usage across their environments ensuring sensitive data is properly managed from model training through production. As a Product Manager II for AI & Data Security, you will own capabilities for AI discovery, posture management, and data security; giving customers complete visibility into their AI applications, agents, and enabling ecosystem, with prioritized actions to operate AI systems securely. You'll partner with engineering, design, security research, and GTM teams to define and ship platform capabilities that help organizations adopt AI at scale. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Own the roadmap for AI & Data Security capabilities, including AI and data discovery & posture management. Define how security teams can assess and manage the security posture of AI-enabled systems, including configuration risks, sensitive data exposure, and policy violations. Work closely with engineers and designers to deliver new product capabilities end-to-end, from early concept through launch and iteration. Partner with Datadog security researchers to identify emerging risks in AI systems and translate them into actionable product features. Engage with customers to understand how they are adopting AI and validate solutions that help them operate these systems securely. Collaborate with go-to-market teams to enable adoption and communicate the value of AI security capabilities to customers. Who You Are: You have 3+ years of product management experience building technical products, ideally in security,

As organizations rapidly adopt AI applications and agentic systems, security teams need visibility and control over how these technologies are being used. Datadog's AI & Data Security product helps customers discover, secure, and govern AI usage across their environments ensuring sensitive data is properly managed from model training through production. As a Product Manager II for AI & Data Security, you will own capabilities for AI discovery, posture management, and data security; giving customers complete visibility into their AI applications, agents, and enabling ecosystem, with prioritized actions to operate AI systems securely. You'll partner with engineering, design, security research, and GTM teams to define and ship platform capabilities that help organizations adopt AI at scale. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Own the roadmap for AI & Data Security capabilities, including AI and data discovery & posture management. Define how security teams can assess and manage the security posture of AI-enabled systems, including configuration risks, sensitive data exposure, and policy violations. Work closely with engineers and designers to deliver new product capabilities end-to-end, from early concept through launch and iteration. Partner with Datadog security researchers to identify emerging risks in AI systems and translate them into actionable product features. Engage with customers to understand how they are adopting AI and validate solutions that help them operate these systems securely. Collaborate with go-to-market teams to enable adoption and communicate the value of AI security capabilities to customers. Who You Are: You have 3+ years of product management experience building technical products, ideally in security,

Datadog is seeking a Director of Product Management to lead our AI Observability portfolio and shape how organizations build, monitor, and scale AI systems in production. This role leads LLM Observability and helps define the next wave of innovation across GPU Monitoring, Distributed AI Monitoring, and emerging research-oriented tooling such as Model Lab. You will set the vision and strategy for this rapidly growing area, expanding established products while incubating new capabilities that deliver deep visibility into AI infrastructure, model performance, and distributed AI environments. As AI becomes core to modern applications, this team plays a critical role in ensuring customers can deploy and scale AI with confidence. We’re looking for a builder-minded product leader with strong technical depth and hands-on curiosity - someone who has built or worked closely with AI-powered products and understands the realities of production AI. You will lead a team of product managers and partner closely with engineering and design to advance Datadog’s leadership in AI observability. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Own the vision and strategy for AI-driven products, ensuring alignment with overall company goals and customer needs. This will include managing our embed program to enhance the capabilities of existing products as well as developing dedicated and independent AI products. Lead and mentor a team of product managers, helping them grow and advance their careers while ensuring the delivery of high-quality, AI-powered features. Collaborate with cross-functional teams including engineering, data science, marketing, and sales to deliver AI product solutions that meet customer needs and business objectives. Identify new opportunities for

Applied AI is where Datadog's ambitious AI bets get built and shipped ( Bits Chat , updog ). We sit at the intersection of research and product: turning promising capabilities from Datadog AI Research lab and the research community into production systems that reach real customers. The team builds specialized models that replace frontier models where they are not necessary, making AI capabilities faster, cheaper, and more secure. The mandate is to move fast from idea to customer impact, and when a product finds its footing, to set it up for growth. As a Manager I in Applied AI, you will lead a team of engineers and applied scientists working on one of these challenges. You will define technical direction, run short feedback loops, make deliberate decisions about what to pursue or stop, and work closely with product managers, research teams, and cross-functional partners to ship AI capabilities that matter. At Datadog, we place value in our office culture, the relationships and collaboration it builds and the creativity it brings. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do Lead and develop a team of engineers and applied scientists focused on cost-efficient specialized models and AI security capabilities Work closely with product managers, research teams, and cross-functional partners to shape the team's bets from initial framing through to broader adoption, with a clear definition of success criteria at each stage Own end-to-end delivery of high-quality AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality Navigate the unique challenges of shipping AI-powered products: balancing quality, latency, cost, and safety considerations. Drive evaluation and iteration practices for AI systems: define the quality bar and guide the team in building the offline

Staff Forward Deployed Engineers (FDE) sit at the intersection of enterprise customer environments and a fast-moving internal product. They are senior technical leaders who own ambiguous, high-impact problems that span customer engagements, product direction, and engineering execution. They are accountable not only for getting customers to production, but for using what is learned in the field to shape reusable product capabilities, technical strategy, and delivery quality across multiple teams. This role is best suited for engineers who want to combine hands-on technical depth with broad influence: defining or changing strategy when customer reality reveals a gap, guiding technical decisions across teams, and translating field insight into durable product and engineering improvements. We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Responsibilities Customer and business impact Own the most complex customer engagements and technical workstreams, especially where the problem, solution, or path to production is ambiguous Build deep understanding of customer architecture, business goals, constraints, and definition of success, and use that understanding to shape the technical approach rather than only execute within a predefined one Act as a trusted technical counterpart to customer’s engineering leaders, internal product and engineering leaders, and executive stakeholders when navigating complex tradeoffs, risks, and priorities Technical leadership and execution Lead architectural design for customer-facing solutions and platform capabilities within the FDE domain, balancing hands-on implementation with technical decision-making across multiple teams Take ambiguous, high-level problems and turn them into clear technical direction, implementation plans, milestones, and ownership boundaries that enable execution at quality and speed Continue to write and review production-quality code, troubleshoot complex failures, and set

Staff Forward Deployed Engineers (FDE) sit at the intersection of enterprise customer environments and a fast-moving internal product. They are senior technical leaders who own ambiguous, high-impact problems that span customer engagements, product direction, and engineering execution. They are accountable not only for getting customers to production, but for using what is learned in the field to shape reusable product capabilities, technical strategy, and delivery quality across multiple teams. This role is best suited for engineers who want to combine hands-on technical depth with broad influence: defining or changing strategy when customer reality reveals a gap, guiding technical decisions across teams, and translating field insight into durable product and engineering improvements. We are looking to speak to candidates who are based in New York City, NY for our hybrid working model. Responsibilities Customer and business impact Own the most complex customer engagements and technical workstreams, especially where the problem, solution, or path to production is ambiguous Build deep understanding of customer architecture, business goals, constraints, and definition of success, and use that understanding to shape the technical approach rather than only execute within a predefined one Act as a trusted technical counterpart to customer’s engineering leaders, internal product and engineering leaders, and executive stakeholders when navigating complex tradeoffs, risks, and priorities Technical leadership and execution Lead architectural design for customer-facing solutions and platform capabilities within the FDE domain, balancing hands-on implementation with technical decision-making across multiple t

The Infrastructure Engineering team is responsible for building and maintaining a self-service internal development platform that enables MongoDB engineering teams to reliably deploy and operate their own production services and products. We work with numerous engineering teams across the company to understand their infrastructure requirements and development workflows, develop broadly applicable self-service platform services and tooling, continuously monitor how platform services are being utilized, and look for ways to improve developer productivity through automation and education. We are big open source enthusiasts and use a number of open source tools in our stack (contributing upstream whenever possible). Some of the tools we use regularly include Go, AWS, Kubernetes, Crossplane, Terraform, Helm, Drone, Prometheus, and Grafana. However, technology is nothing without a stellar team of engineers that are focused on doing high quality work and working as a team to solve complex distributed computing and platform engineering problems. This is where you come in! We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Our ideal candidate Has built and operated large-scale distributed systems in cloud providers (AWS strongly preferred) Has a strong backend programming background. Fluency in Go is strongly preferred; deep experience with another compiled or strongly-typed backend language is acceptable Has experience working with AI coding agents and can demonstrate building high quality context to yield high quality outputs Has experience designing and implementing medium-to-large software projects, including driving design reviews and mentoring less-senior engineers Pragmatic, detail-oriented, self-motivated, and understands the benefits of collaboration Strong experience operating production Kubernetes clusters, not just deployed to it Has practical experience defining and operating against SLI/SLOs for services they owned Str

We are seeking a Senior Site Reliability Engineer to join our growing Gurugram Products & Technology team to provide technical direction, shape architecture, and build key operational foundations of a new platform we are building to make it easier for customers to build AI applications using MongoDB. As a Senior Site Reliability Engineer on this new team, you will be responsible for enabling deployment at scale of AI applications and improving the performance, scalability, and reliability of the distributed systems infrastructure for this new product. The platform's SRE team owns the operational foundations: the Kubernetes fleet, networking, observability and alerting, and tenant isolation. MongoDB engineering teams pride themselves on building high-quality software and living MongoDB cultural values every day – we value intellectual curiosity and honesty, and building together in an environment that prioritizes collaboration over competition. We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Position Expectations Operate and improve the multi-tenant Kubernetes infrastructure that runs customer workloads Build for reliability, making services and infrastructure available, resilient, fault-tolerant, and self-healing Identify and configure key metrics to detect incidents and quantify service health, availability, and performance Participate in a 24/7 on-call rotation to resolve issues involving platform infrastructure Mentor early-career SREs and contribute to the team’s operational practices as it grows Qualifications Strong background in software development and operating distributed systems 6+ years of experience building and operating distributed systems, with proficiency in Python, Go, or a similar programming language Experience operating Kubernetes in production and debugging below the abstraction layer, including scheduling, cluster networking, and node-level issues Expertise in cloud infrastructure platforms, in

Senior Forward Deployed Engineers (FDE) sit at the intersection of enterprise customer environments and a fast-moving internal product. They partner directly with customers to design, build, troubleshoot, and improve production solutions, and they are ultimately accountable for helping customers get to production. This role is best suited for engineers who want to own technical outcomes end to end: understanding what a customer is trying to build, shipping the integration, and feeding learnings back into the product roadmap. This role will be based remotely in the United States (East Coast). Responsibilities Customer success Serve as the primary technical owner for customer engagements from initial discovery through production rollout Understand each customer's architecture, constraints, and definition of success, and drive toward that outcome Manage expectations, communicate risks clearly, and help customers navigate technical decisions with confidence Technical integration Build the connectors, pipelines, and supporting tooling needed to make the platform work inside real enterprise environments Write production-quality code, troubleshoot issues, and implement fixes directly in active workstreams Work effectively within customer environments that have different stacks, infrastructure, and integration constraints Product feedback loop Capture product feedback with precision, including logs, reproduction steps, and a clear proposed path forward Use customer engagements to identify product gaps, surface recurring patterns, and help improve the product roadmap Document technical decisions and tradeoffs clearly so product and engineering teams can extend the work Workstream collaboration Partner closely with product and engineering teams to help turn field patterns into reusable product capabilities Contribute directly in focused workstreams by helping drive technical design, implementation, and delivery Make sound engine

Senior Forward Deployed Engineers (FDE) sit at the intersection of enterprise customer environments and a fast-moving internal product. They partner directly with customers to design, build, troubleshoot, and improve production solutions, and they are ultimately accountable for helping customers get to production. This role is best suited for engineers who want to own technical outcomes end to end: understanding what a customer is trying to build, shipping the integration, and feeding learnings back into the product roadmap. We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Responsibilities Customer success Serve as the primary technical owner for customer engagements from initial discovery through production rollout Understand each customer's architecture, constraints, and definition of success, and drive toward that outcome Manage expectations, communicate risks clearly, and help customers navigate technical decisions with confidence Technical integration Build the connectors, pipelines, and supporting tooling needed to make the platform work inside real enterprise environments Write production-quality code, troubleshoot issues, and implement fixes directly in active workstreams Work effectively within customer environments that have different stacks, infrastructure, and integration constraints Product feedback loop Capture product feedback with precision, including logs, reproduction steps, and a clear proposed path forward Use customer engagements to identify product gaps, surface recurring patterns, and help improve the product roadmap Document technical decisions and tradeoffs clearly so product and engineering teams can extend the work Workstream collaboration Partner closely with product and engineering teams to help turn field patterns into reusable product capabilities Contribute directly in focused workstreams by helping drive technical design, implementation, and delivery Make sound engineering decisions under

Cloud Operations Engineers are responsible for building internal tools and process automation. Day-to-day duties are creating and monitoring systems alert dashboards, reviewing critical event and system logs, accessing customer instances that underpin their production databases, and performing server administration duties including performance troubleshooting. Applicants must be critical thinkers who are quick to detect, resolve, or escalate issues that are sometimes broad in scope and difficult to trace. We are looking for a Lead with strong technical leadership experience as well as technical depth who is looking to collaborate closely with Cloud Operations Engineering Management in building and maintaining a high-performing team that delivers high quality outcomes while fostering psychological safety and professional growth. We are looking to speak to candidates who are based in Dublin for our hybrid working model. Core responsibilities Team leadership: partner with and assist COE Management with the tasks of providing ongoing technical feedback to engineers, support their growth and creating an inclusive team environment Execution and delivery: play a key role in guiding team members through project deliverables ensuring high quality outcomes while also assisting in meeting or resetting timelines when required Time management: between assisting team members with day to day tasks ranging from incident to project management Cross-functional collaboration: work closely with Product, Technical Services and R&D to surface team’s pain points and drive alignment with the goal of providing an excellent user experience to the end customer Coordinate with Lead counterparts within Cloud Operations as well as Technical Services to ensure our uptime guarantees to the MongoDB Atlas customer base Assist and collaborate with the team on scoping, designing, deploying and ongoing maintenance of systems that focus on reducing mean time to resolve customer incidents Detec

The Infrastructure Engineering team is responsible for building and maintaining a self-service internal development platform that enables MongoDB engineering teams to reliably deploy and operate their own production services and products. We work with numerous engineering teams across the company to understand their infrastructure requirements and development workflows, develop broadly applicable self-service platform services and tooling, continuously monitor how platform services are being utilized, and look for ways to improve developer productivity through automation and education. We are big open source enthusiasts and use a number of open source tools in our stack (contributing upstream whenever possible). Some of the tools we use regularly include Go, AWS, Kubernetes, Crossplane, Terraform, Helm, Drone, Prometheus, and Grafana. However, technology is nothing without a stellar team of engineers that are focused on doing high quality work and working as a team to solve complex distributed computing and platform engineering problems. This is where you come in! We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Our ideal candidate 2+ years of experience managing and mentoring a team of 3+ engineers Has 5+ years of experience owning the design and implementation of large software/infrastructure projects Has built and operated large-scale distributed systems in cloud providers (AWS strongly preferred) Has a strong backend programming background. Fluency in Go is strongly preferred; deep experience with another compiled or strongly-typed backend language is acceptable Pragmatic, detail-oriented, self-motivated, and understands the benefits of collaboration Strong experience operating production Kubernetes clusters, not just deployed to it Has practical experience defining and operating against SLI/SLOs for services they owned Strong experience with observability tooling: metrics, logging, traces, Prometheus, Grafana, OpenTe

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . As a Solutions Architect on the Sales, Trading and Prime team, you'll help scale our B2B products by bridging technical field engagement with hands-on software development. You'll work directly with customers, from startups to large institutions, as a trusted technical advisor while contributing to open source projects and extending Coinbase's core products to unlock strategic client opportunities. This role involves substantial coding, and candidates must be actively developing and delivering software in their current role. What you’ll do: Partner with customers as a trusted technical advisor, guiding them in building on Coinbase services and APIs and deepening relationships as they scale. Collaborate with sales teams to address prospect technical requirements and support deal closure. Architect distributed systems solutions for complex customer needs and build open source SDKs and reference implementations. Lead customer implementations end-to-end, delivering technical presentations, resolving roadblocks, and partnering with support teams to ensure success. Drive collaboration with product and engineering teams to extend core capabilities for strategic client opportunities. Required skills and experience: Degree in computer science and 3+ years of software engineering experience, including expert-level proficiency with REST and WebSocket APIs and production experie

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . We're hiring a Staff Analytics Engineer to lead the data foundations for the Compliance Data team within the Platform organization. This team owns the Compliance Reporting Data Platform, Coinbase's canonical source of truth for user, transaction, and compliance data, and every regulatory report at Coinbase depends on it. You'll define the technical direction for how compliance data is modeled, validated, and certified at scale, ensuring our foundations hold as products, entities, and regulatory requirements evolve, while also delivering accurate, timely data when regulators come calling. What you'll do: Own the systems, architecture, technical strategy, and roadmap for the Compliance Data, including the data models, pipelines, quality frameworks, and certification processes that the entire compliance reporting stack depends on. Build production-grade solutions, in addition to data models and pipelines, covering user, transaction, and compliance data across retail and institutional product lines, designing for durability, auditability, and consumption by both analysts and AI agents. Drive data quality at the source by establishing data contracts, validation frameworks, monitoring, and reconciliation logic that catch issues before they propagate to downstream reports or regulatory filings. Partner with upstream engineering teams, Compliance leadership, and downs

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. With a billion rides per year and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Rider, Marketplace, Growth, and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a next-generation platform for low-cost, ultra-immersive transportation to improve people's lives warrants modern ML utilizing peta-byte scale data. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business. If you are a critical thinker with experience in machine learning workflows and LLMs, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you. We are seeking a Senior Machine Learning Engineer to join the Rider Applied AI team and lead the design, development, and deployment of state-of-the-art machine learning and artificial intelligence systems. This role requires a strategic thinker who can balance high-level system architecture with hands-on technical implementation. You will collaborate across teams to shape the future of ride-sharing by leveraging AI, Machine learning and Data science. Responsibilities: Model Development & Research: Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions. System Design: Architect scalable, reliable ML pipelines that integrate seamlessly with existing backend systems. Innovation & Applied Research: Stay ahead of the curve by exploring emerging algorithms, technologies (such as LLMs and LLM-based applications), and frameworks — critically eva