דרושים » תוכנה » Staff ML Engineer

we are on a mission to create public transportation systems that provide far greater access to jobs, healthcare, and education. Our platform serves as the technology backbone for modern transit networks, transforming antiquated and siloed public transportation systems into smart, data-driven, and efficient digital networks. With hundreds of agency partners around the world, we are recognized as the leading transportation technology and service provider globally.
As a Software & Data Engineer at our company, you will play a central role in building and evolving the data infrastructure that powers one of the world's leading transportation platforms. You'll work with rich, complex transportation data - from ride bookings and payments to real-time operational signals - designing the pipelines and systems that turn raw data into clean, reliable, and actionable insight across the business. This is a high-ownership role on a talented and deeply motivated engineering team in Tel Aviv, where your work will directly shape how our company understands and improves its service for cities and riders around the world.
About the Role:
Design and build highly scalable, reliable data pipelines that serve clean, structured data across our company's engineering, product, and business teams - ensuring the entire organisation can trust the data it works with
Own the architecture of complex data models that translate messy, real-world transportation data - bookings, payments, driver activity, and more - into systems that are fast, efficient, and built to scale
Lead end-to-end development across the full data lifecycle: from architecture and design through to deployment, monitoring, and continuous improvement
Proactively monitor data quality and reliability, identifying and resolving discrepancies before they reach stakeholders - building the kind of data infrastructure people can depend on
Collaborate with a broad forum of engineers, analysts, and business stakeholders to identify data needs, design POCs, and ship scalable solutions that make a real difference to how our company operates
Contribute to the adoption and evolution of our modern data stack, including DBT, Airflow, Iceberg storage, and AWS big data tooling such as Glue, EMR, and Athena.

we are on a mission to create public transportation systems that provide far greater access to jobs, healthcare, and education. Our platform serves as the technology backbone for modern transit networks, transforming antiquated and siloed public transportation systems into smart, data-driven, and efficient digital networks. With hundreds of agency partners around the world, we are recognized as the leading transportation technology and service provider globally.
As a Front-End Engineer at our company, you'll be an integral part of one of our engineering teams. You will build the technology powering our companys Operation Center - the real-time heartbeat of our global service. This is a rare opportunity to own mission-critical systems that shape the daily lives of hundreds of thousands of riders. You will have frontend ownership, working with a sophisticated stack and a team that values high-quality craft. We are looking for a high-agency engineer who leverages modern AI to outpace traditional workflows and is ready to grow into a formal leadership role
About the Role:
Design and implement user-facing features using React and TypeScript for our companys Operation Center.
Own the frontend architecture for new components and workflows, ensuring clean, scalable, and maintainable code.
Accelerate development by leveraging modern AI coding tools to optimize your workflow and output.
Collaborate cross-functionally with product managers, designers, and engineers to deliver end-to-end features
Step into leadership by mentoring peers, driving technical decisions, and shaping front-end best practices.

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.



As a ML Engineer, you will:



Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.

we are using technology to transform transportation around the world. From changing a single persons daily commute to reducing humanitys collective environmental footprint - weve got huge goals.
As a Senior Full Stack Engineer, you will build the technology powering our companys Operation Center - the real-time heartbeat of our global service. This is a rare opportunity to own mission-critical systems that shape the daily lives of hundreds of thousands of riders. You will have full-stack ownership, working with a sophisticated stack and a team that values high-quality craft. We are looking for a high-agency engineer who leverages modern AI to outpace traditional workflows and is ready to grow into a formal leadership role.
About the Role:
End-to-End Ownership: Lead the development of our companys real-time operation center, managing complex routing, driver dispatching, and mass-scale user communication.
Full-Stack Architecture: Architect and ship features across our stack (React/TypeScript/Redux and Python microservices) with an emphasis on performance and scalability.
AI-Augmented Development: Integrate AI-driven tools (Copilot, Cursor, LLM-based testing/refactoring) into your daily workflow to increase velocity and maintain elite code quality.
Future Leadership: Mentor junior/mid-level engineers, drive technical roadmaps, and set the standard for engineering excellence within the team.
Strategic Collaboration: Work directly with Product and Design to translate ambiguous operational challenges into elegant, scalable technical solutions.

We're looking for a Backend Engineer to help build and scale the Machine Learning Platform that powers how our company uses AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
We believe three things matter for every role at our company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll
Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units
Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management
Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring
Evaluate and choose the right tools and technologies based on workload demands and performance requirements
Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals
Identify and fix reliability, scalability, and performance gaps before they become problems.

As a Machine Learning Engineering Manager, you will lead a team focused on the foundational ML & Data layers to power the ranking & recommendation systems in scope. You will drive the development of robust data & ML pipelines at scale, lead the implementation of the tools for ML scientists to test and productionize advanced ML RecSys solutions.

As a technical manager of Machine Learning Engineers and Data engineers, you should be passionate about technology, keep up to date with recent breakthroughs in the field, define and shape the teams ML and platforms roadmap, and not be afraid to get your hands dirty with code when needed.

You are expected to be the focal point for all technical aspects, make sure your team members deliver on their tasks, and work together with other stakeholders to define and shape the roadmap of our products. You will work independently and will also be responsible for making technical decisions within your team.

When it comes to management, your expertise in handling people will motivate and inspire them to reach outstanding success! You should have experience in developing people. You will mentor and coach your team while working closely with a Product Manager.



Key Job Responsibilities and Duties:

Lead and develop a high-performing team, fostering individual growth and collaboration.

Manage and mentor ML engineers and Data engineers, ensuring their professional development and effectiveness.

Develop scalable ML infrastructure and pipelines for efficient data processing and evaluations deployment.

Evaluate architecture solutions based on cost, business needs, and emerging technologies.

Collaborate closely with software engineers to ensure seamless deployment and model inference.

Monitor application health, set and track relevant metrics, and implement effective maintenance strategies.

Collaborate with stakeholders to translate business requirements into viable ML solutions.

Evaluate and integrate new ML technologies to enhance productivity and performance.

At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we pioneered the world's first fully automated suite of AI-powered vehicle inspection systems, combining computer vision, machine learning, and generative AI.
With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone.
We are looking for an experienced Senior Deep Learning Engineer with proven capabilities to lead the development of state-of-the-art computer vision products from concept to production. In this role, you will be a part of a highly talented R&D team driving the core of UVeye’s inspection portfolio, while researching and implementing diverse Machine and Deep Learning algorithmic solutions to solve complex, real-world visual challenges.
A day in the life and how you’ll make an impact:
* Design and Development of novel, robust and efficient Machine Learning/ Deep Learning algorithms for major Vision challenges.
* Create Proof of Concepts (PoC) for new products and technologies, transitioning research-grade ideas into scalable, production-ready systems.
* Evaluate, benchmark, and profile different algorithms on unique, large-scale proprietary datasets, while driving data-centric AI methodologies (active learning, data curation).
* Collaborate with infrastructure, MLOps, and hardware teams to define future technology, features, and optimize models for real-time inference constraints (on Edge/Cloud devices).

We are looking for a Staff Engineer to join our Core Engineering group. The group is responsible for the real-time, low-latency infrastructure that powers our fraud decisions and external APIs.

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.

Were looking for our first Forward Deployed Engineer (FDE) to join our company and help build this function from the ground up.
This is a highly technical, hands-on, and customer-facing role. Youll be based in Israel, working closely with our Product and Engineering teams, while traveling regularly across the U.S. to work on-site with our enterprise customers.
Youll work directly with customers to understand their business processes, technical environments, and operational challenges, and translate them into AI-powered solutions. Youll own projects end-to-end - from discovery and requirements gathering through technical design, development, deployment, adoption, and continuous improvement.
As our first FDE, youll have a unique opportunity to define how our company works with customers from a technical perspective, build the processes and best practices for this function, and play a key role in how we scale customer delivery.
Were looking for a strong engineer with a product mindset who enjoys navigating ambiguity, working directly with customers, and turning complex business problems into scalable technical solutions.
our Values
Ownership - We take responsibility and move decisively.
Clarity - We simplify complexity to deliver meaningful impact.
Accuracy - Precision matters in everything we do.
Velocity - We move fast and execute with purpose.
Partnership - We succeed by working closely together.
What Youll Do
Partner directly with enterprise customers, both remotely and on-site across the U.S., to deeply understand their business processes, technical architecture, data flows, and operational challenges.
Lead technical discovery sessions, gather business and product requirements, and translate complex customer needs into scalable AI-powered solutions.
Design, build, configure, and deploy production-ready AI agents, integrations, and workflows that operate reliably within customer environments.
Work hands-on with APIs, enterprise systems, structured and unstructured data, and customer infrastructure to deliver end-to-end solutions.
Own customer engagements from technical discovery through implementation, deployment, adoption, and continuous optimization.
Validate solutions using real customer data, troubleshoot technical challenges, and continuously improve system performance and business outcomes.
Drive measurable business impact by identifying new use cases, increasing platform adoption, and helping drive growth and expansion within customer accounts.
Work closely with Product and Engineering in Israel to influence product direction based on customer feedback, implementation learnings, and real-world use cases.
Act as the technical owner throughout the customer engagement, building trusted relationships with both technical and business stakeholders.
Create reusable implementation playbooks, tools, and best practices that help scale the FDE function as our company grows.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.