דרושים » תוכנה » senior ML engineer- 2545

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.
ML is central to our 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?
Youll 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 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. 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 Senior ML Engineer - Applied AI Engineering Group
The Dream Job
It starts with you - an engineer driven to build the ML platform that turns research into reliable, production-grade intelligence. You care about reproducibility, low-friction experimentation, and infrastructure that earns the trust of the scientists and researchers who depend on it daily. You'll architect and ship our ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - turning models into production capabilities across cloud and on-prem, including air-gapped deployments. A significant part of the platform supports large language models, with unique challenges across training, evaluation, and inference in mission-critical environments.
If you want to make a meaningful impact, join our mission and build the ML platform that drives Sovereign AI products - this role is for you.
The Dream-Maker Responsibilities
Build and operate ML training infrastructure - distributed training pipelines, compute scheduling, and reproducible experiment workflows that data scientists rely on daily.
Own model serving and inference systems - packaging, deployment, autoscaling, A/B testing, canary rollouts, and latency/cost optimization for production models.
Run feature stores, model registries, and dataset versioning - enabling self-serve feature engineering, model lineage, and reproducible experiments across teams.
Build experiment tracking and evaluation infrastructure - automated evals, comparison dashboards, drift detection, and monitoring that give teams visibility into model behavior and performance.
Build and maintain production pipelines for training, fine-tuning workflows, and serving domain models - owning reliability, reproducibility, and scale.
Build and maintain the monitoring and observability layer - model performance tracking, data and prediction drift detection, data quality validation, and alerting.
Improve performance and cost across the ML stack - training throughput, inference latency, batch vs. real-time tradeoffs, and compute cost management.
Ship shared tooling - libraries, templates, CI/CD for models, IaC, and runbooks - while collaborating across Data Platform, AI, Data Science, Engineering, and DevOps. Own architecture, documentation, and operations end-to-end.

As an ML Software Engineer with a focus on low-level and CUDA-based optimizations, you will play a key role in shaping the design, performance, and scalability of machine learning inference systems. Youll work on deeply technical challenges at the intersection of GPU acceleration, systems architecture, and ML deployment.
Your expertise in CUDA, C/C++, and performance tuning will be crucial in enhancing runtime efficiency across heterogeneous computing environments. Youll collaborate with designers, researchers, and backend engineers to build production-grade ML pipelines that are optimized for latency, throughput, and memory use, contributing directly to the infrastructure powering next-generation AI products.This role is ideal for an engineer with strong systems-level thinking, deep familiarity with GPU internals, and a passion for pushing the boundaries of performance and efficiency in machine learning infrastructure.

Responsibilities
Design and implement highly optimized GPU-accelerated ML inference systems using CUDA and low-level parallelism techniques
Optimize memory, compute, and data flow to meet real-time or high-throughput constraints
Improve the performance, reliability, and observability of our inference backend across diverse compute targets (CPU/GPU)
Collaborate with cross-functional teams (including researchers, developers, and designers) to deliver efficient and scalable inference solutions
Contribute to ComfyUI and internal infrastructure to improve usability and performance of model execution flows
Investigate performance bottlenecks at all levels of the stack-from Python to kernel-level execution
Navigate and enhance a large, complex, production-grade codebase
Drive innovation in low-level system design to support future ML workloads

We are looking for a skilled and highly motivated senior software engineer to join and play an integral role on the Runtime team, part of the MLOps group at JFrog ML. This team is responsible for building high-quality, enterprise-grade deployment and serving infrastructure for classic ML models and LLMs - ensuring they are efficiently deployed, monitored, and scaled to handle millions of requests across multiple cloud platforms, including GPU-powered machines. As a core member of the team, youll tackle meaningful and engaging challenges across a diverse technological stack.

We value creative, fast learners and independent thinkers who are proactive, collaborative, and capable of driving tasks from concept to production

As a Senior Software Engineer at JFrog ML you will...
Be an integral part of a highly skilled team working to build the leading MLOps platform in the industry
Maintain and evolve the Runtime teams products, ensuring their reliability and scalability.
Design and develop a complete hosting system that supports various types of inference, analytics, monitoring, distribution, and more - enabling customers to run large-scale real-time, batch, and streaming ML pipelines
Play a key role in shaping our cross-company engineering culture
Conduct high-quality design reviews with a strong emphasis on scalability, maintainability, security, and sound use of design patterns
Write maintainable, well-tested code in multiple programming languages.
Continuously improve the efficiency, scalability, and stability of critical system components

Required ML Data Engineer
Israel: Tel Aviv/ Hybrid (Israel)
R&D | Full Time | Job Id: 24792
Key Responsibilities
Your Impact & Responsibilities:
As a Data Engineer - AI Technologies, you will be responsible for building and operating the data foundation that enables our LLM and ML research: from ingestion and augmentation, through labeling and quality control, to efficient data delivery for training and evaluation.
You will:
Own data pipelines for LLM training and evaluation
Design, build and maintain scalable pipelines to ingest, transform and serve large-scale text, log, code and semi-structured data from multiple products and internal systems.
Drive data augmentation and synthetic data generation
Implement and operate pipelines for data augmentation (e.g., prompt-based generation, paraphrasing, negative sampling, multi-positive pairs) in close collaboration with ML Research Engineers.
Build tagging, labeling and annotation workflows
Support human-in-the-loop labeling, active learning loops and semi-automated tagging. Work with domain experts to implement tools, schemas and processes for consistent, high-quality annotations.
Ensure data quality, observability and governance
Define and monitor data quality checks (coverage, drift, anomalies, duplicates, PII), manage dataset versions, and maintain clear documentation and lineage for training and evaluation datasets.
Optimize training data flows for efficiency and cost
Design storage layouts and access patterns that reduce training time and cost (e.g., sharding, caching, streaming). Work with ML engineers to make sure the right data arrives at the right place, in the right format.
Build and maintain data infrastructure for LLM workloads
Work with cloud and platform teams to develop robust, production-grade infrastructure: data lakes / warehouses, feature stores, vector stores, and high-throughput data services used by training jobs and offline evaluation.
Collaborate closely with ML Research Engineers and security experts
Translate modeling and security requirements into concrete data tasks: dataset design, splits, sampling strategies, and evaluation data construction for specific security use.

We are looking for a Senior Software Engineer to join our Decision Engineering team. The group is responsible for the real-time, low-latency infrastructure that powers our fraud decisions and external APIs.
Our systems process billions of requests every day, ensuring high availability, security, and performance at global scale.
In this role, youll work on core backend components such as our decision engine, ingestion and enrichment pipelines, schema management systems, and self-serve API platform. The software you build will power critical business decisions and directly serve some of the worlds largest merchants.
This is a high-impact, high-ownership position for an engineer who thrives on solving complex distributed systems challenges, cares deeply about production-grade quality, and wants to shape the foundation of our decisioning platform.
What you'll be doing:
Design, build, and scale backend systems that power our real-time decisioning and APIs.
Own projects end-to-end - from design and implementation to production rollout and monitoring.
Ensure systems are low-latency, fault-tolerant, and high-throughput across distributed environments.
Enhance observability, reliability, and developer experience through strong operational and tooling practices.
Collaborate with Product, analysts, data scientists, and infrastructure teams to drive innovation across our decision ecosystem.
Participate in technical discussions and customer interactions, providing expertise and clear communication when supporting enterprise integrations.

Required Senior ML Engineer
Responsibilities:
Work on our core data and machine learning infrastructure, which is at the heart of our offering. You will create innovative solutions for data ingestion and normalization from multiple data sources, feature engineering and feature selection, as well as actual model training and evaluation. All in large scale and completely automated.
Who You Are:
A problem solver at heart, you have a passion for excellence, you love to learn but know when its time to deliver and make ends meet. You arent threatened by a complex, dynamic and demanding environment. There is no I in team, is a motto you believe in deeply and you are always looking out for your peers. You know how to take ownership and drive projects to completion.

We're in search of an experienced and skilled Senior Data Engineer to join our growing data team. As part of our data team, you'll be at the forefront of crafting a groundbreaking solution that leverages cutting-edge technology to combat fraud. The ideal candidate will have a strong background in designing and implementing large-scale data solutions, with the potential to grow into a leadership role. This position requires a deep understanding of modern data architectures, cloud technologies, and the ability to drive technical initiatives that align with business objectives.

Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.

Your Arena

Design, develop, and maintain scalable, robust data pipelines and ETL processes
Architect and implement complex data models across various storage solutions
Collaborate with R&D teams, data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality solutions
Ensure data quality, consistency, security, and compliance across all data systems
Play a key role in defining and implementing data strategies that drive business value
Contribute to the continuous improvement of our data architecture and processes
Champion and implement data engineering best practices across the R&D organization, serving as a technical expert and go-to resource for data-related questions and challenges
Participate in and sometimes lead code reviews to maintain high coding standards
Troubleshoot and resolve complex data-related issues in production environments
Evaluate and recommend new technologies and methodologies to improve our data infrastructure

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.
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.