Key Responsibilities
We are looking for a passionate Software Developer to join our WAF SaaS Platform team, where we build, evolve, and operate our companys cloud-native WAF SaaS product.
In this role, you will develop new SaaS product capabilities while also building and operating the cloud infrastructure that powers the platform. You will work across backend services, Kubernetes environments, automation, CI/CD, monitoring, and, when needed, frontend components.
We are looking for a strong team player with a high sense of ownership, a get-things-done mindset, and the willingness to contribute across all areas of development - from UI and backend to DevOps and cloud infrastructure.
Major Responsibilities
Develop new features and capabilities for our companys WAF SaaS product, with a focus on backend services, cloud infrastructure, and DevOps-oriented development
Design, build, and maintain cloud-native infrastructure that supports customer environments, production workloads, and global SaaS deployments
Take part in creating and operating new POPs, improving customer environment provisioning, and evolving the platforms scalability and availability
Develop SaaS product capabilities such as certificate management, CDN-related flows, DDoS-related capabilities, onboarding flows, and other customer-facing platform features
Work with cloud services, CI/CD pipelines, monitoring, logging, and automation tools
Take end-to-end ownership of features and infrastructure components, from design and implementation through deployment, observability, support, and production operation
Build and maintain dashboards, alerts, and operational tools, using data and metrics to improve reliability, performance, and product decisions
Collaborate closely with developers, DevOps engineers, product managers, security researchers, support teams, and other stakeholders
Support production environments, investigate customer issues, and understand the real-world impact of features, bugs, and infrastructure changes.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

We are looking for a highly experienced Quantum Control Systems Engineer (Application Validation Team) who embodies ambition and positivity. Someone who can passionately take ownership of their responsibilities, collaborating effectively with remote teams to not only meet but exceed our objectives and fulfill the evolving needs of our expanding customer base.
As a Quantum Control Systems Engineer on the Application Validation Team, you will be responsible for validating the software and hardware systems that drive quantum computing operations, ensuring that programs ranging from calibrations to error correction procedures perform as expected on all our quantum controller hardware. This includes designing, implementing, and testing scalable software and hardware frameworks across various qubit types, as well as developing tools that support the validation and execution of control software across different configurations and execution platforms.
Your role will also involve developing a robust software infrastructure that automates the validation process, integrates with real hardware systems, and supports continuous integration (CI) and continuous deployment (CD) pipelines.
This is an exciting opportunity to work at the forefront of quantum computing technology while ensuring the performance, reliability, and scalability of cutting-edge control systems.
Join us in this exciting journey to redefine the future of quantum computing.
Responsibilities:
Quantum Computing Routine Validation: Validate quantum computing programs on our quantum controllers, including calibration procedures, error correction routines, and other algorithms, ensuring they meet the required performance standards.
Scalable Routine Design: Design and implement scalable quantum routines in Qua and other quantum computing languages, optimizing various qubit types and quantum computing architectures.
Framework Development: Develop tools and frameworks that support validation, ensuring scalability and integration into our hardware and software development process.
Automated Testing and CI/CD Integration: Build and maintain automated testing systems, incorporating CI/CD processes to ensure seamless integration and validation of quantum control software on real hardware.
Data Analysis: Analyze measurement data from quantum controllers, using this data to improve implementations across the whole software stack.
Cross-Functional Collaboration: Work closely with quantum physicists, customers software engineers, and hardware engineers to troubleshoot issues, refine quantum routines, and ensure the overall system's performance.

As a Senior DevOps Engineer, youll help turn agentic AI capabilities for diagnosing and troubleshooting network and GPU infrastructure into secure, scalable, production-ready services. This role stands out through its end-to-end ownership across cloud and customer-managed environments, close partnership with software and AI engineers, and direct influence on the reliability of NVIDIAs AI infrastructure.


What You'll Be Doing:
Own the DevOps, infrastructure, security, release, and reliability lifecycle - from development environments and CI/CD through deployment, production readiness, and sustained operations.
Build and operate Kubernetes environments and Helm-based deployments for a Python, FastAPI, Node.js, and React microservices platform across SaaS and on-premises footprints.
Engineer GitLab CI/CD pipelines with automated testing, container builds, vulnerability scanning, and versioned image and Helm chart publication through JFrog Artifactory.
Automate infrastructure provisioning, configuration, upgrades, and routine operational workflows to accelerate delivery and improve engineering productivity.
Operate PostgreSQL, Temporal workflow services, and S3-compatible object storage with disciplined capacity planning, backups, recovery testing, and safe migrations.
Strengthen release reliability through deployment validation, reduced-downtime strategies, persistent-state protection, and recovery plans for active workflows.
Deliver actionable observability and security using OpenTelemetry, Datadog/Grafana, Langfuse, secrets management, identity integration, TLS, Kubernetes RBAC, network policies, and container hardening.
Partner with software and AI engineers to troubleshoot distributed systems, investigate incidents, define reliability targets, and improve platform performance, resource efficiency, and customer outcomes.

Were looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
Youll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. Youll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
Develop and improve our core algorithms.
Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices-not just standalone prototypes.
Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.

As the Development Team Lead for HiredScore Sourcing Products, you will manage and mentor a cross-functional team of Full-Stack Engineers, Backend Engineers, and QA Automation Specialists.
Your team's core mission will be to translate AI/ML insights into seamless, scalable features that drive efficiency in recruiter workflows-specifically through resurfacing past applicants, prospects, and internal talent. You will own the entire development lifecycle for your products, from core API implementation to deploying user-facing applications and integration layers (web UIs, RPA/workflows, MS Teams bots)
Key Responsibilities:
Lead, mentor, and grow the diverse engineering team, ensuring effective collaboration between backend, full-stack, and QA functions. Drive best practices in development, code quality, and delivery speed.
Design, document, and oversee the implementation of scalable, reliable, and secure systems, ensuring seamless integration between backend AI services and the user-facing product layers.
Ensure the team maintains a strong quality and automation culture by providing strategic direction to QA specialists and upholding high standards for testing and product stability.
Drive development across the full spectrum of product surfaces: APIs (GraphQL/REST), Web Applications (React/Angular/Vue), and specialized engagement layers.
Ensure efficient consumption of AI outputs by downstream systems, particularly through robust queue-based architectures (like RabbitMQ) and workflow automation (RPA).
Partner closely with Product Management, Data Science, and other engineering teams to translate business requirements into technical execution.

We are looking for a strong team player with a high sense of ownership, a "get-things-done" mindset, and the willingness to contribute across all areas of development - from UI and backend to data and detection pipelines.
Major Responsibilities
Develop new features and capabilities for our company's Gen AI Security SaaS product, with a focus on backend services and real-time detection & prevention pipelines
Develop SaaS product capabilities such as prompt-injection and jailbreak detection, content moderation, PII and data-leakage prevention, policy and practice management, and other customer-facing platform features
Contribute to our native inspection agent that runs close to customer traffic
Contribute to the platform's ML pipeline, including data collection and feature engineering that support the platform's detection models
Work with cloud services, CI/CD pipelines, monitoring, logging, and automation tools
Take end-to-end ownership of features, from design and implementation through deployment, observability, support, and production operation
Build and maintain dashboards, alerts, and operational tools, using data and metrics to improve reliability, performance, and product decisions
Collaborate closely with developers, DevOps engineers, product managers, security researchers, support teams, and other stakeholders
Support production environments, investigate customer issues, and understand the real-world impact of features, bugs, and infrastructure changes.

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

We are at the forefront of the AI revolution, delivering brand new accelerated compute platforms for global impact. Our Network Architecture group is seeking a talented and motivated Sr. Software Engineer to build the agentic workflows that our architects use in their daily work. The software at the center of this role is our hardware network simulation environment - you will design multi-step agent workflows over it, engineer the context that grounds them in our own specifications and source code, and optimize their runtime performance. If you are passionate about building the practical infrastructure that brings intelligent agents to life, we want to hear from you.


What you'll be doing:
Build agentic workflows - loops, graphs, and multi-step pipelines - that carry real hardware network simulation and analysis work end to end.
Engineer the context these workflows run on, turning our simulation models, specifications, design documents, and source code into context that makes agents accurate in our domain.
Work closely with network architects to understand their workflows and translate them into agent workflows they use daily.
Optimize the runtime performance of our simulation tooling on these platforms, including execution time, compute cost, and end-to-end latency.
Define evaluation and regression testing for agent workflows, so that changes to a prompt, a graph, or a context source are measurable.
Build observability across agent runs: what the agent did, where it failed, and why.
Champion guidelines for secure and reliable agent workflows, including data handling, access control, and interaction boundaries.
Serve as a key technical resource for solving sophisticated integration issues between agents and internal tooling.