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 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 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're looking for a Platform Team Leader to own the foundational systems that power R&D organization. The Platform team is a horizontal, internal-facing team - its customers are the engineers, data scientists, and analysts on our application teams, and its mission is to make them faster, safer, and more productive.

You will lead a team responsible for our production-critical data ingestion (scraping system, data normalization platform), our developer experience surface (Python mono repo, packages and services framework, CI/CD, shared images), our Airflow base layer, and our backoffice. These systems sit on the critical path for production - every other team depends on them, and uptime, incident response, and reliability are core to the role.

This role is for a hands-on technical leader who enjoys both shipping code and growing engineers. You'll spend roughly 50% of your time hands-on writing code, reviewing designs, and the rest leading the team, partnering with peer team leads, and shaping the technical direction of our platform with the VP R&D and CTO.

Responsibilities
Lead and grow a team of strong backend engineers, owning hiring, mentorship, performance, and personal growth
Own the team's roadmap end-to-end: scoping, prioritization, delivery, and quality
Drive the architecture and evolution of the platform layers your team owns - both the data plumbing that feeds and the developer-experience surface other R&D teams build on
Contribute hands-on (~50%) to design, code, code reviews, and production debugging
Set and uphold engineering standards across the team - code quality, system design, testing, observability, and operational excellence
Partner closely with peer team leads and the VP R&D to align on shared infrastructure, ownership boundaries, and developer experience
Lead by example on production incidents and customer-impacting issues
Influence broader R&D direction as a member of the R&D leadership forum

Required Developer Experience (DevEx) Team Lead
As a Developer Experience (DevEx) Team Lead, you'll lead the team responsible for the platforms, tooling, and workflows that power our engineering organization. You'll help hundreds of engineers build, test, deploy, and operate software more efficiently by removing friction from the software development lifecycle. This is a leadership role where you'll shape technical direction, grow a high-performing team, and drive initiatives that improve engineering productivity across the company.
You'll Own:
Developer Experience Platform- Drive the evolution of our internal developer platform, enabling self-service capabilities across local development, CI/CD, testing, deployment, and observability.
Team Leadership & AI Adoption- Lead, mentor, and grow a team of platform and software engineers while driving the adoption of AI-powered development tools that accelerate engineering productivity at scale.
Engineering Productivity- Own the reliability and scalability of CI/CD systems, improve software delivery workflows, and partner with Infrastructure, Security, AI Enablement, and R&D leadership to continuously raise engineering effectiveness.
You'll Solve:
Remove bottlenecks across the software development lifecycle by building scalable platforms, automation, and self-service solutions that enable engineers to move faster with confidence.
Evaluate, operationalize, and scale emerging AI capabilities while ensuring they are adopted safely, effectively, and in ways that create measurable impact across R&D.
You'll Impact:
Increase engineering productivity and developer satisfaction by delivering reliable internal platforms and workflows that support hundreds of engineers across the organization.
Shape how software is built by influencing engineering standards, improving delivery speed and quality, and enabling teams to innovate more efficiently.
*We operate in a flexible hybrid work model.

Required Engineering Team Lead - Data Science Team
About the Role:
A top player in its game and is heading for an exciting year.
In this role, you will take an integral part in the creation of a new and innovative SaaS product.
If you are a talented software engineer and engineering manager that likes to solve complex problems and looking for your next challenge we want you to join our team!
What youll do:
As the Team Leader of a Detection and Response Engineering team, you will lead a talented team to design, implement, and deploy scalable data solutions for an innovative SaaS product. Youll be responsible for shaping the direction of a system that impacts millions of users worldwide. If you're an experienced manager with a strong technical background, ready to lead in a dynamic, fast-paced production environment, we want you on our team!
The group responsible for 4-5 different products:
1. DRS (fully managed fraud detection solution)
2. Agentic Visibility
3. Citadel (ITP) - lean version of DRS capabilities
4. Transaction Monitoring offering
5. More might come soon
Responsibilities:
Lead and guide one of the DRS Engineering teams in the design and deployment of scalable data infrastructure.
Collaborate effectively with cross-functional teams (Data Science, Research, Security, etc.) to ensure alignment on business needs and security standards.
Manage the technical backlog, make critical decisions on product milestones, and ensure timely, secure, high-quality delivery and deployment.
Foster the growth of team members through mentorship, personal development opportunities, and the promotion of continuous improvement.
Identify, manage, and mitigate security risks across the data platform, maintaining high standards in all initiatives.
Define and implement methodologies to guarantee the performance, scalability, and quality of our data infrastructure.

we're looking for super-talented people, who are not afraid of technical challenges and breaking down barriers to achieve good solutions.



About the Role

We are looking for an experienced full-stack team lead to join our R&D organization, lead the design and development of large-scale, data-oriented applications.
In this role, you will manage a talented team of full-stack engineers while remaining hands-on and technically involved in building our core SaaS security platform.
You will play a key role in shaping system architecture, driving technological innovation, and ensuring the delivery of robust and scalable solutions in a fast-paced, dynamic environment.


Key Responsibilities

Lead and mentor a team of full-stack engineers, guiding them in design, development, and delivery of complex projects.
Drive the architecture and implementation of data-centric applications using modern technologies and cloud infrastructure.
Lead a fast-moving development team, ensuring strong execution and teamwork in a dynamic environment
Collaborate with QA, Product, Design, and DevOps teams to ensure system scalability, reliability, and performance.
Conduct code reviews, enforce best practices, and foster a high-quality engineering culture.
Research and integrate new technologies to overcome technical challenges and improve productivity.
Own full project lifecycle, from requirement gathering and design to deployment and monitoring in production.

Were seeking a Senior Machine Learning Engineer with strong MLOps abilities to own the production ML systems behind user engagement, cardiovascular risk stratification, and personalized health recommendations in the app. You'll build and run the CI/CD, real-time serving, versioning, and monitoring infrastructure that models depend on, working closely with data scientists who own the modeling to make sure what they build runs reliably at scale. You should have a proven track record of shipping ML systems to production and be comfortable using AI coding assistants as a core part of your workflow.
Responsibilities:
Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.
Serve models in real time at scale, with a strong focus on low-latency inference, reliability, and graceful degradation under load, and operate batch inference with clear standards for latency, availability, correctness, and cost.
Write high-quality, maintainable, well-tested production-grade code, and own its observability, debugging, reliability, and scalability in production.
Implement model monitoring for drift, data quality, latency, and performance degradation, with automated retraining.

This is a leadership role within our companys core AI group, focused on building and evolving the AI-facing software systems that enable our algorithms to move from research into robust, production-ready components.
The AI Software team is responsible for developing the software layers, tooling, and core systems that support the development, execution, and operational readiness of AI models throughout their lifecycle. As a Team Lead, you will shape the teams technical direction, guide execution, and remain deeply hands-on, contributing to architecture, implementation, and key technical decisions.
You will lead a scrum team, work closely with AI researchers and algorithm engineers, and collaborate with product and engineering partners. The role combines strong software engineering with AI context, in a culture where software quality is a core enabler of AI excellence, and technical leadership is grounded in ownership, clarity, and impact.
Responsibilities
Lead the teams work on core software systems that sit at the intersection of algorithms and engineering, owning their design and implementation as part of the overall solution.
Guide and support team members growth, providing technical leadership, mentoring, and day-to-day coaching while fostering ownership and accountability.
Stay deeply hands-on in design and implementation, contributing code and solving complex software engineering problems where impact is highest.
Collaborate closely across teams, working with algorithm engineers, Product, and Engineering partners to define interfaces and align priorities.
Own project-level architecture and technical decisions, ensuring high-quality systems through sound design, code reviews, testing practices, and clear documentation.

Engineering Team Lead - Application Infrastructure
As an Application Infrastructure Team Lead, youll spearhead the development of the core frameworks and services that power our entire ecosystem. In this role, youll sit at the vital intersection of application development and systems engineering, ensuring our platform remains resilient, high-performing, and developer-friendly as we scale.
If you are a technical heavyweight who loves Java but thrives on leading teams through complex data and infrastructure puzzles, this is your opportunity to build the foundation for our next phase of growth.
Youll Own:
Team leadership & mentorship: Guiding a high-performing engineering team and fostering a culture of ownership and excellence.
Core application infrastructure: Designing and maintaining mission-critical frameworks using Java and Spring Boot.
Technical standards: Defining and enforcing the benchmarks for code quality, system performance, and security across the entire stack.
Streaming & messaging pipelines: Owning the reliability and scalability of asynchronous communication using Kafka or SQS.
Youll Solve:
Data layer optimization: Leading specialists to tackle challenges in high availability and performance across PostgreSQL, MongoDB, OpenSearch, and Redis.
Distributed systems complexity: Designing robust pipelines for asynchronous communication and data streaming at scale.
Architecture gaps: Bridging the gap between DevOps and Product teams to align infrastructure capabilities with evolving business needs.
Youll Impact:
Engineering velocity: Providing the foundational building blocks that empower all product engineering teams to build faster and better.
System reliability: Ensuring the high availability, scalability, and security of mission-critical systems.
Technical culture: Driving a mindset of continuous improvement and technical excellence that resonates across the R&D organization.