Appdome's mission is to protect every mobile app in the world and the people who use them. We are the leader in AI-native mobile business protection, providing cyber and fraud teams with an agentic platform that builds, monitors, and maintains security defenses in Android and iOS apps — with no SDKs, no coding, and no disruption to engineering cycles.
Our platform delivers over 400 security, anti-fraud, anti-bot, and API protection capabilities, powered by deep learning models trained on a decade of mobile defense data and trillions of live threat events. From build time to runtime, Appdome's AI Agents help mobile brands detect, investigate, and respond to threats faster than ever — recognized as the best AI Platform for Cyber Resilience at RSA Conference 2026 for the second consecutive year. Leading financial, healthcare, m-commerce, and B2B brands rely on Appdome to secure over 50,000 mobile apps and protect more than 1 billion end users globally.
Appdome is an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics.
About the Role We are looking for a talented Software Engineer to join Appdome's Identity Group. You will take part in building advanced fraud detection capabilities, working closely with security researchers and data teams to develop cutting-edge solutions that protect hundreds of millions of mobile devices.
Responsibilities
* Design and develop core components of fraud detection systems from early prototyping through production-ready releases.
* Build scalable backend services and distributed systems that process data at scale.
* Work with large-scale datasets and collaborate with data teams to create actionable signals and features.
* Write high-performance, production-quality code in C and C++ optimized for reliability and performance.
* Take ideas from rapid proof-of-concept through to production, tackling complex engineering challenges across the full development lifecycle.

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 seeking talented and passionate Senior Data Engineer to join our Data team. In this pivotal role, you will be instrumental in designing, building, and optimizing the critical data infrastructure that underpins innovative creative intelligence platform. You will tackle complex data challenges, ensuring our systems are robust, scalable, and capable of delivering high-quality data to power our advanced AI models, customer-facing analytics, and internal business intelligence. This is an opportunity to make a significant impact on our product, contribute to a data-driven culture, and help solve fascinating problems at the intersection of data, AI, and marketing technology.

Key Responsibilities
Architect & Develop Data Pipelines: Design, implement, and maintain sophisticated, end-to-end data pipelines for ingesting, processing, validating, and transforming large-scale, diverse datasets.
Manage Data Orchestration: Implement and manage robust workflow orchestration for complex, multi-step data processes, ensuring reliability and visibility.
Advanced Data Transformation & Modeling: Develop and optimize complex data transformations using advanced SQL and other data manipulation techniques. Contribute to the design and implementation of effective data models for analytical and operational use.
Ensure Data Quality & Platform Reliability: Establish and improve processes for data quality assurance, monitoring, alerting, and performance optimization across the data platform. Proactively identify and resolve data integrity and pipeline issues.
Cross-Functional Collaboration: Partner closely with AI engineers, product managers, developers, customer success and other stakeholders to understand data needs, integrate data solutions, and deliver features that provide exceptional value.
Drive Data Platform Excellence: Contribute to the evolution of our data architecture, champion best practices in data engineering (e.g., DataOps principles), and evaluate emerging technologies to enhance platform capabilities, stability, and cost-effectiveness.
Foster a Culture of Learning & Impact: Actively share knowledge, contribute to team growth, and maintain a strong focus on how data engineering efforts translate into tangible product and business outcomes.

We are looking for a Backend Engineer with deep data engineering expertise to build and evolve the systems that power product, analytics, experimentation, and AI development.
You will work across production backend services and data infrastructure, taking technical ownership of the full data lifecycle - from event ingestion and real-time processing to orchestration, modeling, quality, and reliable data access. This role combines hands-on backend development with ownership of shared data platform, working closely with backend, frontend, AI, DevOps, analytics, and product teams.
What you'll do:
Build and maintain production-grade Python and Java backend services, APIs, and reactive data-processing pipelines with clear interfaces, resilient failure handling, and comprehensive observability.
Own and evolve batch and streaming data pipelines supporting product analytics, learner insights, experimentation, and AI model training.
Design and maintain trusted data models, shared metrics, and semantic-layer business logic across the analytical data platform.
Define and enforce standards for data contracts, testing, lineage, freshness, and observability.
Develop reliable approaches to schema evolution, backfills, replay, workload distribution, and failure recovery.
Collaborate with DevOps, backend, frontend, AI, analytics, and product teams to deliver reusable data capabilities and maintain clear system boundaries.

Required Senior Data Engineer
Description
We are making the future of Mobility come to life starting today.
We support the worlds largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization.
At the heart of our platform lies the data infrastructure, driving advanced machine learning models and optimization algorithms. As the owner of data pipelines, you'll tackle diverse challenges spanning optimization, prediction, modeling, inference, transportation, and mapping.
As a Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform-supporting real-time optimization, advanced analytics, and machine learning applications.
What You'll Do:
Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools.
Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets.
Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle.
Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows.
Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability.
Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions.
Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability.

Were looking for a talented Data Engineer to join our AI team and build the data foundations that power our AI systems at scale. As a key member of the AI team, youll be responsible for designing, building, and operating the data layer that enables our AI models and agents to perform in production. This includes data ingestion, storage, transformation, monitoring, and defining the data strategy that ensures our AI applications receive high-quality, reliable, and timely data.

Were seeking an ambitious Data Engineer who is passionate about building robust data infrastructure, working with large-scale and complex datasets, and partnering closely with AI engineers and researchers to translate model needs into production-grade data systems.


WHAT YOU WILL DO
Design, build, and operate scalable data pipelines (batch + streaming) that power our AI systems in production.
Own the end-to-end data lifecycle: ingestion, storage, transformation, serving, and continuous improvement.
Build and maintain the data layer from raw data to semantically accessible data that enables AI models and agents to perform reliably at scale.
Ensure high data quality and high-standard operation through monitoring, alerting, and validation checks.
Work with modern storage systems (data lakes, warehouses, relational, graph, and vector databases) to support diverse AI workloads.
Partner closely with AI engineers and researchers to translate model requirements into production-grade data infrastructure.
Define and evolve the data strategy to for AI applications.

Our Applied Science group builds the intelligence behind the marketplace. We turn large volumes of unstructured data into structured understanding, and use it to connect the right people with the right work. We are a small team with an unusually large surface. We own the full path from raw data to production AI systems, and we are actively expanding what that platform can do. In this role, you will design, build and operate AI systems that run continuously in production: large volumes of unstructured data in, models and pipelines in the middle, structured signal that teams across us build on back out. The scope runs from data pipelines and retrieval infrastructure through to model quality, evaluation and cost. This is a hands on senior role, and you will help define how these systems get built here rather than inherit someone else's answer.
What am I going to do?
* Build and evolve our production LLM pipelines, turning unstructured text into structured data that the product consumes.
* Design and operate large scale batch and near real time data processing, including correctness, freshness and cost.
* Build the retrieval and serving layers that make our data usable in real time.
* Decide what runs on which LLM model, and prove the choice with numbers rather than intuition.
* Keep cost and latency predictable as volume grows: throughput, failure handling and monitoring.
* Build agentic workflows that makes the whole team faster, and help set the standards others work to.
Equal opportunities:
We prioritize diversity. We celebrate difference and embed it into every aspect of our workplace and product, as well as our community. We are proud and committed to providing equal opportunity employment to all individuals regardless of race, color, religion, sex, sexual orientation, citizenship, national origin, disability, Veteran status, or any other characteristic protected by law. In addition, we will provide accommodation to individuals with disabilities or a special need.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

We're looking for an experienced Software Engineer to join our Data Platform Group. In this key role, you will build the company data platform: cloud-based microservices and data pipelines that process on the order of 1M records/sec at low latency. Because the platform is the foundation other groups build on, your work has a direct impact on our customers and enables engineering, product, and research teams across the organization.
The Lakehouse team owns the data itself - how it is stored, organized, retained, and served. We own our analytical storage layer, the batch processing built on top of it, and the APIs through which customers and the rest of the company consume data. If you enjoy the problems that only appear at petabyte scale - physical data layout, query performance, storage cost, and retention - this is the role.

Responsibilities:
End-to-end ownership of our large-scale analytical storage layer: data modeling, schema and table design, partitioning, retention, and query performance.
Design and develop the batch processing layer over our data lake using Spark on EMR (Java and PySpark).
Build and evolve Java/Spring Boot services that expose our data through well-defined APIs to customers and to consumers across the company.
Own performance and cost: query optimization, file layout and compaction, cluster sizing, and storage efficiency at scale.
Research new technologies in the lakehouse and analytical-storage space and adapt them for use in our product.
Work closely with product, DevOps, and security teams.

We're hiring a Data Engineer to architect and own the systems that turn raw signal from merchants, customers, and experiments into something the whole company can build on.
Data is at the heart of everything we do - it powers our AI models, drives merchant-facing analytics, and informs how we make product decisions every day.
This is a high-ownership role for someone who wants to shape data architecture from the ground up- not inherit someone else's roadmap. You'll be the person who makes sure our data is fast, trustworthy, and easy for every team to use.
What You'll Do:
Architect and build scalable data pipelines processing millions of events daily from merchant stores, product usage, experiments, and third-party integrations
Own our data warehouse strategy end-to-end- reliability, performance, and scalability as we grow
Define our event architecture and set the standards for how the company captures and models product and business data
Build data quality practices- monitoring, validation, testing, and alerting- that the rest of the team can trust
Partner directly with Product and Engineering to unlock the data behind our AI features, merchant analytics, and experimentation
Build self-service tooling so other teams can query and analyze data without depending on you as a bottleneck
Set the bar for data modeling, orchestration, and documentation across the org