Required Senior MLOps Engineer
Realize your potential by joining the leading performance-driven advertising company!
As a Senior MLOps Engineer on the Infra group, youll play a vital role in develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools.
About Algo platform:
The objective of the algo platform group is to own the existing algo platform (including health, stability, productivity and enablement), to facilitate and be involved in new platform experimentation within the algo craft and lead the platformization of the parts which should graduate into production scale. This includes support of ongoing ML projects while ensuring smooth operations and infrastructure reliability, owning a full set of capabilities, design and planning, implementation and production care.
The group has deep ties with both the algo craft as well as the infra group. The group reports to the infra department and has a dotted line reporting to the algo craft leadership.
The group serves as the professional authority when it comes to ML engineering and ML ops, serves as a focal point in a multidisciplinary team of algorithm researchers, product managers, and engineers and works with the most senior talent within the algo craft in order to achieve ML excellence.
How youll make an impact:
As a Senior MLOps Engineer Engineer, youll bring value by:
Develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools, including CI/CD, monitoring and alerting and more
Have end to end ownership: Design, develop, deploy, measure and maintain our machine learning platform, ensuring high availability, high scalability and efficient resource utilization
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
Work in tandem with the engineering-focused and algorithm-focused teams in order to improve our platform and optimize performance
Optimize machine learning systems to scale and utilize modern compute environments (e.g. distributed clusters, CPU and GPU) and continuously seek potential optimization opportunities.
Build and maintain tools for automation, deployment, monitoring, and operations.
Troubleshoot issues in our development, production and test environments
Influence directly on the way billions of people discover the internet
Our tech stack:
Java, Python, TensorFlow, Spark, Kafka, Cassandra, HDFS, vespa.ai, ElasticSearch, AirFlow, BigQuery, Google Cloud Platform, Kubernetes, Docker, git and Jenkins.

Required Staff MLOps Engineer
Realize your potential by joining the leading performance-driven advertising company!
As a Staff MLOps Engineer on the Infra group, youll play a vital role in develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools.
About Algo platform:
The objective of the algo platform group is to own the existing algo platform (including health, stability, productivity and enablement), to facilitate and be involved in new platform experimentation within the algo craft and lead the platformization of the parts which should graduate into production scale. This includes support of ongoing ML projects while ensuring smooth operations and infrastructure reliability, owning a full set of capabilities, design and planning, implementation and production care.
The group has deep ties with both the algo craft as well as the infra group. The group reports to the infra department and has a dotted line reporting to the algo craft leadership.
The group serves as the professional authority when it comes to ML engineering and ML ops, serves as a focal point in a multidisciplinary team of algorithm researchers, product managers, and engineers and works with the most senior talent within the algo craft in order to achieve ML excellence.
How youll make an impact:
As a Staff MLOps Engineer Engineer, youll bring value by:
Develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools, including CI/CD, monitoring and alerting and more
Have end to end ownership: Design, develop, deploy, measure and maintain our machine learning platform, ensuring high availability, high scalability and efficient resource utilization
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
Work in tandem with the engineering-focused and algorithm-focused teams in order to improve our platform and optimize performance
Optimize machine learning systems to scale and utilize modern compute environments (e.g. distributed clusters, CPU and GPU) and continuously seek potential optimization opportunities.
Build and maintain tools for automation, deployment, monitoring, and operations.
Troubleshoot issues in our development, production and test environments
Influence directly on the way billions of people discover the internet
Our tech stack:
Java, Python, TensorFlow, Spark, Kafka, Cassandra, HDFS, vespa.ai, ElasticSearch, AirFlow, BigQuery, Google Cloud Platform, Kubernetes, Docker, git and Jenkins.

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.

As a Senior Data Engineer, you will be helping us design and build a flexible and scalable system that will allow our business to move fast and innovate. You will be expected to show ownership and responsibility for the code you write, but it doesn't stop there. you are encouraged to think big and help out in other areas as well.

Responsibilities:

Designing and writing code that is critical for business growth
Mastering scalability and enterprise-grade SAAS product implementation
Sense of ownership - leading design for new products and initiatives as well as integrating with currently implemented best-practices
Building and owning production-grade ETL/ELT pipelines that power analytics, ML training, and real-time AI systems
Designing data architectures that support agentic systems, including:
Embeddings and vector-based retrieval
RAG pipelines
Feedback loops and continuous improvement
Review your peer's design and code
Work closely with product managers, peer engineers, and business stakeholders

We are looking for a Senior Engineer with a data engineering background to join our growing ML Platform team. This is a great opportunity, whether you have experience with ML and are looking for a ML focused product or are an experienced Data Engineer looking to enter the world of ML. Together well provide tools to develop more effective models, get them into production faster, and ensure that they continue to perform well over time.

What you will be doing:
Designing, building, and maintaining the ML infrastructure that allows our models to make billions of real-time decisions every year.
Building a platform that enables managing a full ML model lifecycle - from researching to training, deploying, and serving predictions in real-time.
Building distributed data processing pipelines to support model development.
Acting as a consultant to researchers, data scientists, and expert analysts and enabling them to research new models faster and with greater precision by providing cutting-edge tooling.
Expanding our ML infrastructure to make it scalable, quick, and efficient to bring diverse models to production and to monitor their performance and drift over time.
Expanding the pool of internal customers able to use ML at Forter. Work with them to understand their needs and help them make the most of the infrastructure that well provide.
Acting as an advocate for MLOps, continually improving our processes, and raising our standards.

Required Machine Learning Engineer II - GenAI Applications
26947
About the team:
This opening is for the GenAI Applications Team within the Data & AI Marketplace department.
The GenAI Applications team is responsible for designing and delivering agentic, ML-powered solutions for some of our most impactful products, including booking search experiences, trip planning, and trip helpfulness. The team builds AI-driven applications and conversational agents, such as chatbots and intelligent assistants, that significantly enhance the end-to-end customer experience.
Role Description:
As a Machine Learning Engineer, you will work closely with experienced engineers and ML scientists to build scalable, production-grade GenAI applications. Your work will focus on designing, training, and deploying ML systems leveraging LLMs,, recommendation systems, and agent-based architectures, using state-of-the-art technologies. These solutions will directly power customer-facing experiences and play a key role in shaping the future of AI-driven travel products.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, chatbots, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

We are currently looking for a Senior Machine Learning Engineer to design and implement scalable and integrate machine learning solutions into production. You will work with modern technologies like Node.js, Python, Kafka, MongoDB, and OpenSearch, AirFlow, while driving architectural decisions and CI/CD best practices. This role combines backend engineering excellence with ML deployment expertise.

The Senior Machine Learning Engineer will report directly to the Director of R&D.

Your key responsibilities will include:

Architect and develop MLE services using Python, ensuring high performance, reliability, and scalability.
Collaborate with Data Science teams to productionize ML models (model serving, monitoring, retraining pipelines).
Build and maintain CI/CD pipelines for automated testing, deployment, and monitoring.
Design and implement system architecture for distributed, event-driven systems.
Integrate and optimize Kafka for real-time data streaming and event processing.
Design and manage MongoDB schemas and queries for optimal performance.
Implement and maintain OpenSearch clusters for search and analytics use cases.
Ensure security, observability, and fault tolerance across all services.
Mentor team members and contribute to engineering best practices.

we are looking for an AI Engineer to lead our MLOps team and take full ownership of our machine learning infrastructure used to train, evaluate, and deploy our state-of-the-art computer vision models. In this role, you will work closely with other team leads and Product to plan and build the future of core AI platform. You will help define technical direction, make long-term architectural decisions, and translate company goals into executable plans, directly impacting ability to scale its AI capabilities and the overall success of the company.
Responsibilities

Manage and lead the MLOps team, including mentoring, professional development, and day-to-day technical guidance
Plan and own quarterly roadmaps, sprint planning, task breakdown, and execution, ensuring alignment with company and product priorities
Design, build, maintain, and evolve ML infrastructure for training, evaluation, and deployment of computer vision models
Own the end-to-end ML lifecycle in production, including experimentation workflows, training orchestration, model versioning, deployment, and monitoring
Balance delivery and technical excellence by making architectural decisions and enforcing engineering best practices
Work closely with research, data, product, and platform teams to move models reliably from research to production
Actively contribute code to critical components and review design and implementation across the team
Evaluate and introduce new technologies and tooling to improve scalability, reliability, and developer productivity

It starts with you - a senior ML engineer responsible for building, training, evaluating, and operating machine learning systems in production. The role focuses on data pipelines, model training, experimentation, evaluation, and scalable deployment.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.

We are seeking talented engineers to join our teams in building cutting-edge products that connect billions of people around the world. As a member of our team, you will have the opportunity to work on complex technical problems, build new features, and improve existing products across various platforms, including mobile devices and web applications. Our teams are constantly pushing the boundaries of user experience, and we're looking for passionate individuals who can help us advance the way people connect globally. If you're interested in joining a world-class team of industry veterans and working on exciting projects that have a significant impact, we encourage you to apply.
Software Engineer, Machine Learning Responsibilities
Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences
Implement custom user interfaces using latest programming techniques and technologies
Develop reusable software components for interfacing with back-end platforms
Analyze and optimize code for quality, efficiency, and performance
Lead complex technical or product efforts and provide technical guidance to peers
Architect efficient and scalable systems that drive complex applications
Identify and resolve performance and scalability issues
Work on a variety of coding languages and technologies
Establish ownership of components, features, or systems with expert end-to-end understanding.