The role

Nebius is looking for a Senior Data Scientist (Applied AI / LLM) to design, build, and deploy production-grade AI systems based on large language models. You will focus on turning LLM capabilities into reliable, measurable business solutions, while also contributing to predictive modeling across different business domains.

Your work will include building evaluation frameworks, improving model accuracy, and developing agent-based systems that automate analytical and data workflows (e.g., data quality monitoring). The role emphasizes production readiness, robustness, and real business impact—not experimentation.

You’re welcome to work in our office in Tel Aviv.

Your responsibilities will include: 

• LLM system development. Design and deploy LLM-based solutions (e.g., RAG pipelines, agent workflows) for real business use cases.
• Evaluation & reliability. Build evaluation frameworks to measure accuracy, consistency, and failure modes of LLM systems. Continuously improve performance based on real-world usage.
• Agent-based automation. Develop agentic solutions to automate workflows such as data quality checks, anomaly detection, and reporting.
• Applied predictive modeling. Apply classical ML and statistical methods to business domains such as HR and Finance (e.g., forecasting, classification, risk modeling).
• Production & MLOps. Deploy and maintain models in production, ensuring monitoring, versioning, and scalability.
• Stakeholder collaboration. Translate business needs into AI/ML solutions and communicate trade-offs, risks, and performance clearly.
• Cross-domain contribution. Contribute to adjacent areas (e.g., forecasting models) to ensure team redundancy and shared ownership of critical workflows.

We expect you to have: 

• Experience as a data scientist or applied ML practitioner (5+ years).
• Experience building and deploying LLM-based systems in production.
• Experience working in production environments with model monitoring and iteration.
• Experience with modern data science and ML ecosystems using Python.
• Experience working with large datasets and strong SQL skills.
• Understanding of LLM evaluation, prompt design, and system behavior.
• Strong foundation in statistics and machine learning fundamentals.
• Demonstrated ability to deliver production-grade AI systems end-to-end.
• Demonstrated ability to translate business needs into AI/ML solutions and communicate clearly.
• Working knowledge of spoken and written English.

It will be an added bonus if you have: 

•  Experience with RAG architectures and retrieval systems.
• Experience designing or working with agent-based workflows.
• Experience with MLOps tools and production ML systems.
• Experience working in cloud environments (preferably Azure).

The role

Nebius is looking for a Senior Data Scientist to build and deploy time series forecasting and classical machine learning models that support core business planning and operational decisions. You will own problems end-to-end, from data exploration and feature engineering to model deployment and monitoring, working with large-scale, real-world datasets.

The role emphasizes strong statistical thinking, robust modeling, and production reliability over experimentation with trendy frameworks. Your work will directly impact forecasting accuracy, resource planning, and key business metrics.

You’re welcome to work in our offices in Tel Aviv.

Your responsibilities will include: 

• Time series forecasting. Design, build, and maintain forecasting models (e.g., demand, usage, capacity). Evaluate performance using appropriate metrics and improve accuracy over time.
• Classical ML modeling. Develop and deploy models using regression,Binary classifcatiom ,  tree-based methods, and other well-established approaches where they provide the best trade-off between performance, interpretability, and maintainability.
• Feature engineering & data preparation. Build robust pipelines for data cleaning, transformation, and feature generation (including time-based features, seasonality, and external signals).
• Model evaluation & monitoring.Define evaluation frameworks, backtesting strategies, and monitoring to ensure models remain stable and reliable in production.
• Production & MLOps. Deploy models into production environments, collaborate on pipeline orchestration, and ensure reproducibility and version control.
• Exploratory analysis. Analyze large datasets to identify patterns, anomalies, and drivers of change in time-dependent behavior.
• Stakeholder collaboration. Work closely with business and product teams to define forecasting needs, align on success metrics, and translate model outputs into actionable insights.
• Cross-domain contribution. Contribute to adjacent data science areas (e.g., LLM-based systems) as needed, ensuring shared ownership and continuity across team responsibilities.

We expect you to have: 

• Experience as a data scientist working in production environments (5+ years).
• Experience with time series modeling and forecasting techniques.
• Experience building reliable data pipelines and working with large datasets.
• Experience with modern data science tools and ecosystems using Python (e.g., NumPy, Pandas, Scikit-learn, deep learning frameworks).
• Strong SQL skills and experience working with large datasets.
• Strong foundation in statistics and machine learning fundamentals.
• Background in Computer Science, Statistics, Mathematics, Industrial Engineering, Economics, or a related field (Master’s degree or equivalent).
• Demonstrated ability to take models from idea to production and ensure ongoing value.
• Demonstrated ability to apply structured thinking and robust evaluation in forecasting problems.
• Strong communication skills and ability to explain model outputs, uncertainty, and limitations.
• Working knowledge of spoken and written English.

It will be an added bonus if you have: 

• Experience working in cloud environments (preferably Azure).
• Experience with MLOps tools and production model lifecycle management.
• Experience with workflow orchestration tools such as Apache Airflow.
• Familiarity with LLM-based systems (e.g., evaluation, RAG pipelines) in applied settings.

Cato Networks is the first company to converge enterprise networking and security into one centralized and global service that is delivered by cloud. It is led by networking and security pioneer Shlomo Kramer (Check Point, Imperva) and early investor (Palo Alto Networks, Exabeam, Trusteer and more). Cato’s unique technology inspired a brand-new product category, later named “SASE” by Gartner and a market expected to reach $28.5 billion by 2028. 
 
This is your opportunity to get on the rocket ship and join a company that is building a cutting-edge enterprise network and secure cloud platform, and is on a fast track to becoming the worldwide market leader – don’t miss it! 

We’re looking for a hands-on Agentic AI Engineer to build practical, high-impact AI solutions that transform how our teams operate. 

This is not a research role. You’ll design and ship real AI systems that improve workflows across Sales, Marketing, HR, and Operations - building custom solutions where off-the-shelf tools fall short. 

If you enjoy turning messy business problems into production-ready AI systems using LLMs,SLMs, agents, and smart data pipelines - this role is for you. 

Responsibilities: 

• Partner directly with business teams to identify automation and optimization opportunities 
• Design and implement agent-based AI workflows to automate internal processes end-to-end 

• Design and build LLM-powered tools (agents, workflows, copilots) using frameworks like LangGraph / LangChain 

• Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows 

• Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations 

• Take solutions from idea → prototype → production 

• Governance, Reliability & Security 

• Ensure AI workflows comply with security, privacy, and compliance requirements 

• Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed 

• Monitor AI performance, errors, hallucinations, and drift 

• Collaboration & Enablement: 

• Partner with business owners to identify automation opportunities 

• Translate business requirements into AI-driven solutions 

• Document AI flows, decision logic, and operational runbooks 

• Educate internal teams on AI capabilities and limitations 

Requirements: 

• 3 years of hands-on software development experience, including writing, maintaining, and delivering production-quality code 
• 2+ years of GenAI development experience, with strong AI/ML focus - MUST 
• Strong Python skills and production mindset - MUST 
• Hands-on and deep understanding with LLMs, SLMs, prompt engineering, context engineering and agent-based systems 
• Experience with LangGraph, crewAI, Strands or similar orchestration frameworks 
• Experience in enterprise grade agentic solutions and bringing Agents into production 
• Experience in AWS Agent Core - Advantage 
• Solid understanding of ML workflows and MLOps principles 
• Strong analytical skills and ability to work directly with non-technical stakeholders 
• Builder mindset: proactive, independent, and impact-driven 
• Experience with Agentic AI driven applications like n8n, UiPath, Make –Advantage  

Join Apiiro — Design, develop, and deliver secure software faster

Apiiro is a fast-growing startup at the forefront of the application security field. Our cutting-edge platform is designed to help development teams build secure software quickly, enabling them to move faster while managing risks. As pioneers in the app-sec space, we’re shaping the future of secure software development. If you're looking for an exciting opportunity to make a significant impact and grow with a passionate team, Apiiro is the place to be.

About the Team

Our team focuses on delivering cutting-edge, AI-powered automated application security features within our ASPM product. We are a dynamic, agile team emphasizing seamless transitions from design to production, deployment, and ongoing monitoring. We excel at operationalizing machine learning, data science, and AI solutions into robust, secure, and high-performance security capabilities.

What You Will Do

• Be a core member of a cross-functional AI innovation team, collaborating closely with software engineers, MLOps specialists, and analysts.
• Design, build, and optimize end-to-end AI-driven features - from experimentation and model training to validation and deployment.
• Develop production-grade code (Python and C#) to integrate advanced models and data-driven capabilities into our core products.
• Continuously evaluate model performance and data quality in real-world settings, proactively driving improvements, retraining, and robust monitoring

What You’re About

• B.Sc. in Computer Science, Mathematics, or similar.
• 4+ years of hands-on experience in applied data science or machine learning roles.
• Experience with building solutions based on agents, LLMs, generative AI, and advanced AI/ML frameworks.
• Proficient in writing clean, maintainable, and efficient Python code.

• Optional (preferred):
• Experience with additional languages such as Java or C#.
• M.Sc. in Computer Science, Mathematics, or similar.

• Independent, proactive, and agile.
• Skilled at problem-solving and creative solution-finding.
• Strong teamwork and communication skills; thrive in highly collaborative, diverse teams.
• Innovative and committed to continuous learning and staying up-to-date with the latest technologies.

What We Stand For at Apiiro

OneTeam, One Mission means we’re all in it together - tearing down walls, listening to every voice, and working as one to create real value for our customers.

Striving for Greatness drives us to keep pushing forward, aiming higher, and staying resilient. We don’t believe in “good enough.”

Own It, we take pride in what we do. We’re all about accountability, trust, and transparency, so we’re empowered to make a real impact. 

Customer Centric means our customers are at the heart of everything. We go beyond just meeting their needs; we build solutions that truly make a difference.

These values keep us motivated to deliver the best for our customers and each other, every single day.

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If you're ready to join a team that's dedicated to transforming the future of app security, we’d love to hear from you. Apply now and let’s build the next generation of secure software together!

As a Machine Learning Engineer at MyHeritage, you will play a crucial role in developing and implementing machine learning models that drive our products and user experiences. You will have a significant impact on our large and growing user base by leveraging the latest AI technologies to solve challenging problems at scale. This is a unique opportunity to join a forward-thinking team that is passionate about innovation, data, and machine learning.

What you’ll do

• Design, develop, and deploy machine learning models for various business needs.
• Collaborate with cross-functional teams, product managers, and software engineers, to integrate ML solutions into production systems.
• Experiment with and iterate on model architectures to improve accuracy, efficiency, and scalability.
• Work with large datasets to preprocess, clean, and extract meaningful features for model training.
• Utilize MLOps best practices to automate and streamline the development lifecycle of ML models.
• Monitor model performance and fine-tune for optimization.
• Stay updated with the latest research and advancements in machine learning and AI.

What you bring

Technical skills:

• Strong foundation in machine learning techniques such as supervised and unsupervised learning, deep learning, and vision/NLP experience.
• Proficiency in Python, along with popular ML libraries and frameworks such as PyTorch, Scikit-Learn, pandas, NumPy, SciPy, TensorFlow.
• Experience with data preprocessing and feature engineering for structured and unstructured data.
• Knowledge of model deployment and serving, using tools like Docker, Kubernetes, or cloud-based services (AWS, GCP, Azure).
• Familiarity with MLOps tools for automation and monitoring (e.g., MLFlow, ClearML).
• Experience with SQL for data extraction and analysis (optional).

Experience:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 7+ years of experience in software development, including at least 3 years of experience in the machine learning domains.
• Experience working in a B2C company or a similar high-paced environment is an advantage.
• Proven track record of implementing impactful machine learning solutions in production.

Core attributes

• Strong analytical mindset, with the ability to tackle complex problems with creativity and efficiency.
• Excellent communication skills for conveying technical ideas to both technical and non-technical stakeholders.
• High level of attention to detail and quality in your work.
• Ability to work effectively in a team environment, while also being a self-starter when working independently.
• Curious and eager to keep learning about the latest trends in AI and machine learning.
• Comfortable in a fast-paced, dynamic work environment with changing priorities.

 About MyHeritage

MyHeritage is the leading global discovery platform for exploring family history. With billions of historical records and family tree profiles, and with sophisticated matching technologies that work across all its assets, MyHeritage gives users the joy of discovering their past and empowering their future. MyHeritage is the most popular DNA test and family history service in Europe. Since 2020, MyHeritage is also home to the world’s most advanced AI technologies for repairing, enhancing, colorizing, and animating historical photos. Learn more at www.myheritage.com. See what it’s like to work at MyHeritage on www.glassdoor.com.

Benefits at MyHeritage

Our employees enjoy a range of benefits to enhance work-life balance and well-being:

• Hybrid work model
• Modern, beautiful office in Or Yehuda with shuttle service from Tel Aviv and employee parking (for our Or Yehuda employees)
• On-site gym and pilates classes
• Dog-friendly office
• Well-stocked kitchens and a monthly meal allowance 
• Fully funded supplemental health insurance

#LI-Hybrid

Transmit Security gives businesses the modern tools they need to build secure, trusted and end-to-end digital identity journeys to innovate and grow. 

CX-focused, cybersecurity conscious leaders rely on Transmit Security’s xCIAM platform to provide their customers with smooth experiences protected from fraud across all channels and devices.

Transmit Security serves many of the world’s largest banks, insurers, retailers, and other leading brands, collectively responsible for more than $1.3 trillion in annual commerce. 

About the Role:

Transmit Security is 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 that likes to solve complex problems and looking for your next challenge we want you to join our team!

What you’ll do: As the Team Leader at Transmit Security, you will lead a talented team to design, implement, and deploy scalable data solutions for an innovative SaaS product. You’ll 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!

Responsibilities:

• Collaborate effectively with cross-functional teams (Data Science, 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.

What We're Looking For:

• 5+ years of software engineering experience, with at least 2 years in a leadership capacity.
• Extensive knowledge of distributed computing platforms (Flink, Spark, Beam) and cloud platforms (AWS, GCP, Azure).
• Proficiency in programming languages such as Python, Java, Scala, or Go.
• Demonstrated strong leadership abilities, including guiding teams, managing backlogs, and fostering effective cross-functional collaboration.
• Proven experience in managing security risks and ensuring high-quality, scalable, and compliant systems.
• Ability to maintain focus on key priorities and excel in a fast-paced, dynamic production environment.

Advantages:

• Bachelor’s degree in Computer Science or a related field.
• Experience with microservice architecture, data management tools (Kafka, Airflow, MLOps), and CI/CD processes.
• Familiarity with databases (Postgres SQL, Redis) and Kubernetes.
• Background in identity management, risk assessment, or fraud detection.

#LI-AM1 #LI-Hybrid

What is this opportunity?

At Eleos, we build a behavioral health CareOps automation platform that transforms therapy conversations into structured insights and clinical documentation. Our system uses advanced ML and LLM technologies to improve care quality, support therapists’ daily workflows, and reduce documentation time by over 50%.

As a Senior ML Infrastructure Engineer, you will design and build the infrastructure that powers our ML and LLM systems in production. You will develop scalable pipelines, systems, and tools that enable data scientists and AI teams to efficiently develop, test, and deploy models.
Working closely with data scientists, engineers, and product teams, you will ensure our ML capabilities are reliable, scalable, and production-ready—helping bring cutting-edge AI to improve mental health care.
This is a unique opportunity to join a startup with a real impact on thousands of people’s wellbeing and mental health, applying cutting-edge AI technologies to solve meaningful human problems.

How will you contribute?

• Design and build infrastructure and backend services supporting ML and LLM systems
  
  
• Develop and maintain ML training and deployment pipelines
  
  
• Build tooling that enables model experimentation, versioning, and reproducibility
  
  
• Implement CI/CD pipelines for ML workflows and model deployment
  
  
• Support LLM deployment, prompt management, and optimization pipelines
  
  
• Improve reliability, monitoring, and observability of ML systems in production
  
  
• Collaborate with data scientists to productionize models and research prototypes
  
• Ensure secure and compliant handling of sensitive healthcare data
  
  

What qualifications and skills will help you be successful?

• 5+ years of industry experience in ML Infrastructure, Backend Engineering, or related fields
  
  
• Strong Python experience with production-grade systems
  
  
• Experience working with cloud platforms (AWS, GCP, or Azure)
  
  
• Experience with containerization technologies (Docker, Kubernetes)
  
  
• Experience building CI/CD pipelines for ML systems or backend services
  
  
• Experience supporting LLM deployment or ML models in production
  
  
• Familiarity with model versioning, experiment tracking, and ML tooling
  
  

Some nice to haves are:

• Experience with prompt engineering and prompt management
  
  
• Experience with data versioning tools (DVC, Pachyderm, etc.)
  
  
• Experience with MLOps platforms (MLflow, Kubeflow, etc.)
  
  

We are looking for a visionary and technically versatile Data Science Manager to join the Data Science & MLE group within our DS & Analytics organization.

In this pivotal role, you will lead a hybrid team of Data Scientists and Machine Learning Engineers, acting as the bridge between cutting-edge technical innovation and high-level business strategy. You won't just be managing a team; you will be the Technical Lead defining how we solve complex problems-from predicting player behavior to optimizing marketing budgets using the latest in Generative AI.

You will partner closely with Game Directors, Product Managers, and our central analytics teams to drive value across Zynga's diverse portfolio. If you are a leader who is "bilingual" in data-possessing deep Data Science expertise to guide methodology while bringing high MLE and Engineering skills to build scalable, production-ready systems-we want to hear from you.

Key Responsibilities

• Team Leadership & Mentorship: Recruit, retain, and develop top-tier talent within the Data Science & MLE team. Foster an inclusive culture of innovation where technical rigor meets creative problem-solving.

• Technical Direction & Engineering Standards: Act as the hands-on Technical Lead for the domain. You will supervise the end-to-end development lifecycle-from research to production-enforcing high standards and MLOps to ensure our models are scalable and maintainable.

• Strategic DS & Analytics Support: Drive the development of advanced analytical frameworks to solve core business challenges. You will guide the team in applying rigorous statistical and machine learning methods to areas such as Time Series Forecasting, Causal Inference, Marketing Optimization, Root Cause Analysis, and more.

• Strategy & Collaboration: Partner with Game Directors, BI Platform PMs, and embedded analytics teams to define the data strategy and roadmap. You will ensure that our technical initiatives are directly aligned with company-wide goals and solving the right problems.

• Global Alignment: Maintain a tight collaborative network with Data Science & Analytics managers in North America, ensuring global consistency in methodologies, knowledge sharing, and joint development.

Required Qualifications

• Experience: 5–10 years of experience in data science or machine learning roles, with 3+ years of experience in people management.

• Technical Proficiency: Expert proficiency in SQL and Python. You must be capable of writing and reviewing production-grade code and have hands-on experience with cloud environments (GCP, AWS, Databricks).

• Modeling Expertise: Strong background in Classical ML, Deep Learning, and familiarity with Generative AI (LLMs, Multimodal embeddings, Langchain or like technologies).

• Engineering Mindset: Proven ability to bridge the gap between Research and Engineering. Experience deploying models to production and maintaining them is essential.

• Education: Master’s degree in Computer Science, Math, Statistics, or a related quantitative field; a PhD is strongly preferred.

Preferred Qualifications

• Experience in the mobile gaming industry or high-velocity B2C tech environments.

• Passion for gaming and experience playing various game genres.

• Familiarity with modern data platforms (e.g., Vertex AI, Cloud Run, Sage).

What You'll Need to Succeed

• Independence & Proactivity: Ability to operate in a fast-paced, dynamic environment, managing timelines and mitigating risks effectively.

• Data-Driven Storytelling: Fluent English is mandatory. You need the ability to translate complex data science concepts into clear, compelling narratives for non-technical stakeholders to influence decision-making.

• Collaborative Spirit: A willingness to work across borders and time zones, partnering effectively with teams in Tel Aviv and North America.

We are looking for a visionary and technically versatile Data Science Manager to join the Data Science & MLE group within our DS & Analytics organization.

In this pivotal role, you will lead a hybrid team of Data Scientists and Machine Learning Engineers, acting as the bridge between cutting-edge technical innovation and high-level business strategy. You won't just be managing a team; you will be the Technical Lead defining how we solve complex problems-from predicting player behavior to optimizing marketing budgets using the latest in Generative AI.

You will partner closely with Game Directors, Product Managers, and our central analytics teams to drive value across Zynga's diverse portfolio. If you are a leader who is "bilingual" in data-possessing deep Data Science expertise to guide methodology while bringing high MLE and Engineering skills to build scalable, production-ready systems-we want to hear from you.

Key Responsibilities

• Team Leadership & Mentorship: Recruit, retain, and develop top-tier talent within the Data Science & MLE team. Foster an inclusive culture of innovation where technical rigor meets creative problem-solving.

• Technical Direction & Engineering Standards: Act as the hands-on Technical Lead for the domain. You will supervise the end-to-end development lifecycle-from research to production-enforcing high standards and MLOps to ensure our models are scalable and maintainable.

• Strategic DS & Analytics Support: Drive the development of advanced analytical frameworks to solve core business challenges. You will guide the team in applying rigorous statistical and machine learning methods to areas such as Time Series Forecasting, Causal Inference, Marketing Optimization, Root Cause Analysis, and more.

• Strategy & Collaboration: Partner with Game Directors, BI Platform PMs, and embedded analytics teams to define the data strategy and roadmap. You will ensure that our technical initiatives are directly aligned with company-wide goals and solving the right problems.

• Global Alignment: Maintain a tight collaborative network with Data Science & Analytics managers in North America, ensuring global consistency in methodologies, knowledge sharing, and joint development.

Required Qualifications

• Experience: 5–10 years of experience in data science or machine learning roles, with 3+ years of experience in people management.

• Technical Proficiency: Expert proficiency in SQL and Python. You must be capable of writing and reviewing production-grade code and have hands-on experience with cloud environments (GCP, AWS, Databricks).

• Modeling Expertise: Strong background in Classical ML, Deep Learning, and familiarity with Generative AI (LLMs, Multimodal embeddings, Langchain or like technologies).

• Engineering Mindset: Proven ability to bridge the gap between Research and Engineering. Experience deploying models to production and maintaining them is essential.

• Education: Master’s degree in Computer Science, Math, Statistics, or a related quantitative field; a PhD is strongly preferred.

Preferred Qualifications

• Experience in the mobile gaming industry or high-velocity B2C tech environments.

• Passion for gaming and experience playing various game genres.

• Familiarity with modern data platforms (e.g., Vertex AI, Cloud Run, Sage).

What You'll Need to Succeed

• Independence & Proactivity: Ability to operate in a fast-paced, dynamic environment, managing timelines and mitigating risks effectively.

• Data-Driven Storytelling: Fluent English is mandatory. You need the ability to translate complex data science concepts into clear, compelling narratives for non-technical stakeholders to influence decision-making.

• Collaborative Spirit: A willingness to work across borders and time zones, partnering effectively with teams in Tel Aviv and North America.

We are looking for a highly skilled Senior Machine Learning Engineer to lead our transition from on-demand, third-party LLM APIs to a fully self-hosted, scalable model ecosystem.

Our core product is an advanced, agentic support chatbot capable of complex reasoning, API tool calling, database lookups, and orchestrating specialized Small Language Models (SLMs) for targeted NLP tasks. As we scale, our current deployment infrastructure (AWS SageMaker) is becoming unsustainable. You will be responsible for architecting, deploying, and optimizing an infrastructure capable of supporting 50 to 100 distinct models ranging from 100M to 70B parameters.

What You’ll Do:

• Inference Optimization: Deploy and manage large-scale models using high-performance inference engines (like vLLM) to ensure low latency and high throughput for our agentic chatbot.
• Agentic Workflows: Develop and refine the chatbot's agentic capabilities, ensuring reliable tool-use, routing, and interactions between massive LLMs and specialized SLMs.
• Model Fine-Tuning: Design and execute fine-tuning strategies to improve model accuracy on specific domain tasks and tool-calling execution.
• Rigorous Evaluation: Build comprehensive offline and online evaluation frameworks to constantly measure model performance and business impact through structured A/B testing.

What We’re Looking For:

Core Engineering & AI Frameworks

• Strong proficiency in Python and Bash scripting.
• Deep experience with PyTorch and the Hugging Face ecosystem.
• Experience using AI coding assistants natively in the terminal, specifically Claude Code, to accelerate development workflows.

LLMs, Inference & Agents

• Proven experience deploying models using vLLM, TGI, or similar high-performance inference servers.
• Strong fundamental understanding of LLM architectures, attention mechanisms, and generation parameters.
• Hands-on experience building Agentic systems (ReAct, function/tool calling, RAG).
• Expertise in fine-tuning strategies (e.g., SFT, RLHF, DPO) and parameter-efficient techniques (PEFT/LoRA).

Statistics & Model Evaluation

• Offline Metrics: Deep understanding of classification/summarization metrics (Precision, Recall, F1, AUC) and retrieval metrics (MRR, NDCG, Precision/Recall @ k).
• Online Metrics & A/B Testing: Strong statistical foundation to design and analyze A/B tests safely, including the use of t-tests, Mann-Whitney U tests, and bootstrapping techniques.

Bonus Points

• Containerization & Orchestration: Experience with Ray for orchestrating large-scale model deployments across multi-GPU clusters.
• Model Quantization: Experience with memory optimization techniques like AWQ, GPTQ, GGUF, or FlashAttention to fit 70B models efficiently onto hardware.
• API Development: Proficiency in building robust, asynchronous microservices using FastAPI to serve model requests.

• Knowledge of Data Engineering principles: dataset collection, cleaning, processing, and scalable storage.

Experience with core MLOps practices, including dataset versioning (e.g., DVC), experiment tracking (e.g., Weights & Biases, MLflow), and model registries.

About the role:

Forter is looking for a Senior Big Data Engineer to join our growing ML Platform team. This is a great opportunity for an experienced Data Engineer who wants to apply their skills to the world of Machine Learning. Together, we’ll provide the high-throughput tools and distributed systems needed to process billions of data points, ensuring that our models have the feature-rich, reliable data they need to perform in real-time.

ML is central to Forter’s work. It enables us to process billions of dollars worth of e-commerce transactions and identify fraud rings. Precision at scale is crucial—our models depend on engineered features derived from massive datasets. As the volume of data and the complexity of these models grow, we need senior engineers who can solve the "Big Data" challenges inherent in modern MLOps.

Why should you join us?

You’ll be part of a highly proficient engineering team that is the focal point for all ML engineering activity. This role is perfect for a Big Data specialist who wants to own the data-intensive side of ML—building the pipelines and infrastructure that transform raw global traffic into actionable intelligence.

What you will be doing:

• Designing and building the data-heavy components of our ML infrastructure, allowing Forter’s models to make billions of real-time decisions every year.
• Building distributed data processing pipelines that power model training, feature engineering, and offline research at scale.
• Optimizing the "Feature Store" concept—ensuring that the data used for training matches the data used in production for real-time serving.
• Acting as a technical consultant to data scientists and researchers, helping them navigate massive datasets and providing the Spark-based tooling they need to iterate faster.
• Expanding our ML data infrastructure to be scalable and efficient, focusing on the performance of data ingestion and transformation for diverse model types.
• Improving MLOps standards by ensuring data reproducibility, lineage, and quality across the entire model lifecycle.

Who are you?

• 5+ years of experience with large-scale data processing, with deep expertise in Apache Spark.
• 5+ years developing complex software with at least one general-purpose language (preferably Python or Scala).
• Backend and server-side development experience of complex, highly scalable systems.
• Strong understanding of Data Engineering patterns (Partitioning, Sharding, Schema evolution) and how they apply to model performance.
• Interest in Machine Learning concepts—you don't need to be a researcher, but you should understand how data flows into a model.
• Experience working with public clouds (AWS / GCP / Azure).
• Fluent in written and spoken English.

It’d be really cool if you also:

• Are familiar with Databricks or Airflow for orchestrating complex ML workflows.
• Are comfortable in a containerized environment (Kubernetes/Docker).
• Have experience with low-latency, real-time data serving or streaming (Kafka).

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

About the role: 

Forter is 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 we’ll provide tools to develop more effective models, get them into production faster, and ensure that they continue to perform well over time.

ML is central to Forter’s work. It enables us to process billions of $ worth e-commerce transactions, make decisions in real time, identify fraud rings, and quickly detect new attack methods. Precision is crucial - bad decisions by our models cost us directly and put money into the pockets of fraudsters.

Our adoption by merchants around the world provides us with billions of fresh data points each day. Our team of data scientists, analysts, and cyber intelligence specialists continually identify new signals, engineer new features, and research new models. But as the volume of data and the number and complexity of models grows, so do the engineering challenges. 

If this kind of working environment sounds exciting to you, if you understand that Engineering is about building the most effective and elegant solution within a given set of constraints - consider applying for this position.

Why should you join us?

You’ll be part of a highly proficient engineering team that is a focal point for all ML engineering activity, striving to constantly bring innovation and leverage ML capabilities across all company teams and products.

This role presents a unique opportunity to enter the ML domain. For those already experienced in ML infrastructure, it offers the chance to grow within a team that specializes in high-scale, Big Data and ML systems.

What you will be doing:

• Designing, building, and maintaining the ML infrastructure that allows Forter’s 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 we’ll provide.
• Acting as an advocate for MLOps, continually improving our processes, and raising our standards.

Who are you?

• 4+ years experience with large-scale data processing, ideally with Apache Spark.
• 5+ years developing complex software projects with at least one of general-purpose languages (preferably Python, but not a must)
• Backend and server-side development experience of complex, highly scalable systems
• Experienced with machine learning concepts and frameworks.
• Motivation to understand the needs of internal users, provide them with great tooling, and teach them how to use it. 
• Experience working with public clouds (AWS / GCP / Azure)
• Fluent in written and spoken English

It’d be really cool if you also:

• Are familiar with Databricks or Airflow.
• Are comfortable in a containerized environment.
• Have experience with maintaining highly available, low latency, real-time services 

About us:

Digital commerce is built on trust. At every point along the eCommerce journey, businesses must make a critical decision: Can I trust this customer? Answering this simple question accurately and instantly is powerful—it can accelerate revenue growth and strengthen a company’s connection with its customers. How do we do it? Forter was founded on the insight that it's not about what is being purchased, nor where— but who is behind the interaction.

The Forter Decision Engine finds patterns across more than one billion identities in our dataset. We isolate fraudsters and protect customers—ensuring everyone gets the experience they deserve.  Given that trust is central to how we operate, Forter is very much driven by a defined set of values. We attract remarkable talent and have retention and engagement levels that are well above benchmarks. We’re meticulous about strengthening our culture as we grow and ensuring this is an environment where people can have outsized impact. 

Trust is backed by data –  Forter is a recipient of over 10 workplace and innovation awards, including: 

• Great Place to Work Certification (2021, 2022, 2023, 2024, 2025)
• Fortune’s Best Workplaces in NYC (2022, 2023, 2024 and 2025)
• Forbes Cloud 100 (2021, 2022, 2023, 2024 and 2025)
• #3 on Fast Company’s list of “Most Innovative Finance Companies” (2022)
• Anti-Fraud Solution of the Year at the Payments Awards (2024)
• SAP Pinnacle Awards “New Partner Application Award” (2023)
• Fintech Breakthrough Awards – Best Fraud Prevention Platform (2023)

Life as a Forterian:

We are a team of over 600 Forterians spread across 3 different continents. Since 2013, we've raised $525 million from investors such as Tiger Global, Bessemer, Sequoia Capital, March Capital and Salesforce Ventures. We're on a mission to bring trust to global digital commerce so that companies like Adobe, ASOS, eBay, Instacart, Priceline and Nordstrom can block fraud, drive revenue and improve customer experience.

At Forter, we believe unique people create unique ideas, and valuable experience comes in many forms. So, even if your background doesn't match everything we have listed in the job description, we still encourage you to apply and tell us why your skills and values could be an asset to us. By welcoming different perspectives, we grow together as humans and as a company.

Forter is an Equal Employment Opportunity employer that will consider all qualified applicants, regardless of race, color, religion, gender, sexual orientation, marital status, gender identity or expression, national origin, genetics, age, disability status, protected veteran status, or any other characteristic protected by applicable law.

If you need assistance or an accommodation due to a disability, please email us at interviewaccommodation@forter.com. This information will be treated as confidential and used only for the purpose of determining an appropriate accommodation for the interview process.

The Axonius Product group is looking for a Principal/Senior Product Manager who is passionate about building top-notch enterprise cybersecurity products. In this role, you will shape emerging AI capabilities, enhancing visibility and insights across complex environments. This is a highly strategic role with significant growth potential, diving deep into technical details and driving cross-functional innovation across Engineering, Sales, and Marketing.

Responsibilities

• AI Product Strategy: Define and drive the product roadmap, including emerging areas such as AI, aligning with overall company goals.
• Lifecycle Execution: Lead the end-to-end delivery of complex AI-security features, from initial conception through technical deep-dives to market launch.
• Customer & Market Analysis: Engage directly with customers to translate "pain points" into actionable product requirements and technical specs.
• Cross-Functional Leadership: Partner with Engineering, Data, and Marketing to ensure the AI security vision is integrated seamlessly into the core Axonius platform.

Minimum Qualifications

• Core PM Experience: 4+ years of hands-on Product Management experience in a B2B SaaS environment.
• AI/ML Proficiency: Proven experience working with AI tools, models, or AI-driven product features.
• Technical Foundation: Strong technical background (ideally hands-on engineering experience) with the ability to dive deep into system architectures.
• Professional Fluency: Full professional proficiency in English with a track record of presenting to senior stakeholders.

Preferred Qualifications

• Cybersecurity Domain: Professional experience in Cybersecurity, Networking, or Cloud Infrastructure.
• Advanced AI Knowledge: Specific experience with LLMs, MLOps, or AI-security frameworks.
• Strategic Growth: Experience leading high-level strategic initiatives with potential for future people management.
• Scale Experience: Proven track record of delivering software products at scale in a fast-paced environment.
• Academic Foundation: Degree in Computer Science, Engineering, or a related technical field.

#LI-HYBRID #LI-ZR1

We offer the industry’s only platform that fuses customer identity and anti-fraud solutions – customer identity management, identity verification, and fraud prevention. 

We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc.

About the Role:

As the AI Backend engineer  at Transmit Security, you will join a team of highly skilled machine learning engineers in developing and deploying advanced AI/ML solutions that power our identity and security products. You’ll utilize technical skills to drive innovation, ensure delivery of high-impact projects, and scale our data-driven capabilities across the organization.

This role requires both strategic thinking and hands-on expertise. You’ll be responsible for shaping the data science roadmap, mentoring a growing team, and collaborating with product, engineering, and business stakeholders to translate business challenges into practical machine learning solutions.

What you’ll do:

• Design, develop, and maintain backend services for AI agents and tool integrations using latest technologies
• Build scalable APIs and microservices that interface with LLMs and AI frameworks
• Implement agent orchestration systems, tool calling mechanisms, and workflow engines
• Optimize performance and reliability of AI-powered applications at scale
• Develop data pipelines for training, evaluation, and monitoring of AI systems
• Integrate with various LLM providers (OpenAI, Anthropic, etc.) and manage API interactions

What you’ll need:

• Excellent coding skills in Python/TypeScript, with at least 5 years of hands-on experience building reliable backend services, agents and tooling. Familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) is a strong advantage.
• Experience designing, deploying, and maintaining production systems that integrate ML components, including APIs, microservices, model serving layers, feature pipelines, monitoring, and CI/CD/MLOps workflows.
• Solid experience with AI related contexts Understanding of prompt engineering and LLM optimization techniques, RAG architecture
• Solid understanding of distributed systems concepts, performance optimization, observability, and operating services at scale.
• Strong communication skills, with the ability to bridge technical, product, and business perspectives.
• Prior experience in cybersecurity, fraud prevention, or identity management is a plus, especially with secure system architectures or ML-augmented decisioning systems.

Advantages

• Experience integrating with LLM APIs (OpenAI, Anthropic Claude, etc.)
• Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT)
• Background in ML/AI concepts and model deployment
• Experience with message queues (RabbitMQ, Kafka) and event-driven architectures
• Experience with function calling and tool use patterns in LLMs

#LI-AM1 #LI-Hybrid 

#LI-TL1 #LI-Hybrid