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we are looking for a Backend Software Engineer to join our growing team. Youll help design and build a complex SaaS platform that integrates with security systems, on-prem tools, and a powerful calculation engine, all while providing the best user experience
. In our team, youll work closely with colleagues across Product, Cyber, DevOps, and QA to continuously deliver impact.
Its a startup vibe inside a big company: fast-moving, hands-on, and full of opportunities to grow.
You'll deepen your skills in architecture, scalability, and secure system design and help shape something that matters.
As a Backend Software Development Engineer, you will:
Plan and design high-quality backend solutions, ensuring quality through strong architecture and comprehensive testing.
Build various core components integrated as a whole product:
Extensive and stable integration solutions to both internal and external cyber sources and systems.
Improve, validate, and innovate algorithms, calculations and analytics throughout all system components and integration points.
Work in a fast-paced and agile work environment with business-oriented delivery focus.
Communicate with team members, other technical teams and management to collect requirements, identify tasks, provide estimates and meet production deadlines
Proactively self-learn, maintain, and troubleshoot all existing system components to ensure smooth and reliable operation.
Review and rewrite legacy code to latest technologies.
R-252004

As a machine learning scientist, your work will focus on building, training and deploying content models (Computer vision, NLP and Generative AI) using the most advanced technologies and models.
You will be responsible for identifying and proposing the most appropriate data sources and modeling techniques to solve complex problems and drive business value.
Key Job Responsibilities and Duties:
Explore and apply state-of-the-art techniques in multimodal machine learning.
Train innovative ML models (NLP, CV, LLM-finetuning), build algorithms, and engineering approaches to drive business impact..
Coding skills: ensure implementation of reusable frameworks (clean and scalable code).
Conduct data analysis with detailed metrics to evaluate models performance, labels quality, features exploration.
Work closely with machine learning engineers to ensure the model's latency/throughput meets product requirements and ensure deployment of your model to production.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

As an Engineering Manager working on the AI Application Platform, you will work at the intersection of scalable backend systems and cutting-edge AI. You will have the opportunity to lead a team building AI Applications and developing our Content Intelligence Platform that enables AI-powered experiences for millions of people. You will tackle complex performance and scaling challenges, and collaborate closely with ML Managers, Engineering Managers and Product Managers to bring intelligent systems into production at scale.
For more insights about the different business units and the scope of their projects and goals, please visit this page.
Key Job Responsibilities and Duties
The Engineering Manager plays a central role in managing, coaching, and fostering the growth of an engineering team. Their primary focus is to ensure that the development of both individual team members and the team as a whole aligns seamlessly with organizational objectives and direction.
Important aspects of the job can include:
Lead and develop a high performing engineering team, fostering a culture of excellence, collaboration, and innovation.
Partner with stakeholders to define, own, and deliver the teams roadmap, aligning technical goals with business priorities.
Drive technical quality and scalability through effective architectural decisions, best practices, and continuous improvement.
Oversee execution and delivery to ensure timely, high quality outcomes with optimized throughput.
Stay ahead of technology trends, guiding the team in adopting tools and methods that enhance productivity and product impact.

As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspectsensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.
Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.
Key Job Responsibilities and Duties:
Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
Dealing with massive textual sources to train GenAI foundation models.
Solving issues with data and data pipelines, prioritizing based on customer impact.
End-to-end ownership of data quality in our core datasets and data pipelines.
Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.
Providing tools that improve Data Quality company-wide, specifically for ML scientists.
Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.
Acting as an intermediary for problems, with both technical and non-technical audiences.
Promote and drive impactful and innovative engineering solutions
Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.