דרושים » תוכנה » Senior ML Data Engineer

Required ML Engineering Team Lead
Why us:
Youll work in an awesome environment alongside some of the most innovative people in the industry, using cutting-edge technologies and tools (video editing, Gen AI, data, etc.). You have the opportunity to directly influence the products and tools used by our clients, including sports giants such as the NBA, Bundesliga, LaLiga, ESPN - and thats just the beginning of what we have to offer! Join us and be a part of the best team in tech as we Fuel the Fandom worldwide.
As a ML Engineering Team Lead, you will lead a multidisciplinary team of algorithm and software engineers responsible for designing, building, and operating the next-generation content creation engine. The team focuses on leveraging agentic systems and advanced ML technologies to orchestrate complex, end-to-end production workflows at scale.
In this role, you will translate high-level product and business goals into robust, production-ready solutions, working hands-on across the full lifecycle - from research and system design to deployment, monitoring, and continuous improvement. You will collaborate closely with Product, Platform, Data, and other R&D teams in a fast-paced, agile environment, ensuring that algorithms and software are seamlessly integrated into real-world, high-throughput systems.
You will combine technical leadership, architectural thinking, and people leadership to drive innovation, execution, and delivery, while shaping how intelligent, automated content is created and scaled across our products.
What youll do:
Lead and mentor a multidisciplinary team of algorithm and software engineers, fostering ownership, technical excellence, and professional growth.
Own the delivery of complex, end-to-end systems for automated content creation, from early design and experimentation through production deployment, monitoring, and iteration.
Drive the design and orchestration of agentic systems, working hands-on with the team to integrate ML models, business logic, and software infrastructure across research, engineering, and production at scale.
Translate product and business challenges into technical solutions, defining system architecture, prioritizing initiatives, and guiding execution in an agile environment.
Ensure production readiness and reliability, overseeing deployment, monitoring, performance, and continuous improvement of algorithms and services in a live environment.
Collaborate closely with Product, Platform, Data, and other R&D teams, aligning technical execution with product strategy and business goals in a multidisciplinary setting.
Continuously evolve technically and professionally, encouraging knowledge sharing, joint problem-solving, and awareness of emerging trends in ML, agentic systems, and large-scale software architectures.

We are looking for a Data Engineer to join our Object Detection group and play a key role in managing and enabling data workflows that power our computer vision algorithms. Our team generates and processes large volumes of diverse data from multiple sources, and we need someone who can bridge the gap between raw data and algorithmic innovation. Working alongside algorithm developers and researchers, you'll be responsible for building robust data pipelines, ensuring data quality, and making complex datasets accessible and actionable for our detection models. This role requires both strong coding abilities and a deep understanding of data - you'll need to think like an engineer while understanding the unique needs of a research and development-focused algorithmic group.
What will your job look like:
Work on large-scale systems handling diverse data from multiple sources
Build and maintain robust data pipelines that enable algorithmic development
Evaluate various optional technologies and take part in decision-making
Own key features and services within the data infrastructure
Ensure data quality and accessibility for detection models and research needs
Work onsite in Jerusalem at least one day a week.

We are looking for a DataOps Engineer to own the infrastructure that powers our large-scale data processing platform. This is a platform-facing role sitting at the intersection of data engineering and infrastructure - you'll be the person who makes Spark run reliably and efficiently on Kubernetes, so that data engineers can build with confidence.
You understand data workloads deeply enough to make smart infrastructure decisions, and you have the production instincts to keep complex systems healthy at scale. If you get excited about shaving minutes off Spark job runtimes, right-sizing cluster autoscalers, and building the internal tooling that makes a data platform feel effortless, this role is for you.
RESPONSIBILITIES:
Design, deploy, and operate the Kubernetes-based infrastructure that runs Apache Spark and large-scale data processing workloads
Own the reliability, performance, and cost-efficiency of the data platform - including SLAs, autoscaling, resource quotas, and workload isolation
Manage Spark-on-K8s configurations, Airflow infrastructure, and Databricks integration; tune for throughput, latency, and cost
Build and maintain CI/CD pipelines and infrastructure-as-code for data platform components
Develop observability tooling - metrics, logging, alerting, and data quality dashboards - to proactively surface issues across the pipeline stack
Collaborate closely with Data Engineers to understand workload patterns and translate them into infrastructure decisions
Manage cloud storage (GCS/S3), Delta Lake, and Unity Catalog infrastructure
Drive platform improvements end-to-end: from design through deployment and ongoing ownership.

We are building a centralized intelligence system for the entire organization - a system that collects, consolidates, and analyzes all organizational data, and actively drives business workflows and decision-making.
This role exists to take that system to the next level.
As the Lead Organizational Intelligence Engineer, you will own the evolution of an internal platform that already influences how decisions are made across the company - and turn it into a foundational capability that fundamentally reshapes how the organization operates. Youll work end-to-end: from data ingestion and modeling, through analytics and AI-driven insights, all the way to decision workflows used by executives, R&D, product, and go-to-market teams.
You will operate as a senior individual contributor with broad organizational influence, while laying the technical and product foundations for a future team.
What Youll Do:
Own the architecture and implementation of a company-wide organizational intelligence system, end-to-end
Design and build data pipelines that ingest and unify data from across the organization and into our own platform
Define and evolve data models and semantic layers that create a shared, consistent language across teams and domains
Build advanced analytics and insight mechanisms that move beyond dashboards into decision-enabling workflows
Work directly with company executives to co-define requirements, identify blind spots, and translate strategic questions into systems and capabilities
Apply a product-driven mindset: deciding not just how to build, but increasingly what should be built and why
Leverage frontier AI tooling and agent-based workflows to accelerate development, improve system quality, and expand whats possible
Write and own production-grade code, setting a high bar for quality, reliability, and scalability
Influence technical and organizational workflows across the company to enable deeper intelligence and better decisions
Lay the groundwork for future externalization of these capabilities into customer-facing products

We are looking for an AI Engineer to play a central role in building, evaluating, and advancing our companys AI models. You will own and evolve our companys benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our companys benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.

Our CTO Group is looking for an outstanding Physical-AI Applied Researcher to join our team.
The CTO Group is a small, elite research unit shaping the next generation of algorithmic foundations behind our autonomous driving systems. The group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy.
We are seeking a researcher who thrives at the intersection of machine learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems.
In this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. You will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. The problems are open-ended, scientifically challenging, and deployed at unprecedented scale.
This is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. If youre excited about pushing the boundaries of learning-based decision-making, wed love you to join us and help shape the future of Physical AI.
What will your job look like:
Design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
Advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
Integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
Formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
Lead research directions from conception to full-scale production.
Develop using Python (PyTorch or similar frameworks) as well as C++/GPU/Cuda.

Required AI Algorithm Engineer - Localization Team
Ramat Gan
Full time
You will be part of our REM (Road Experience Management) department, which is responsible for the automatic High-Definition map-making process, which is a key technology in our autonomous driving and high-end Driving Assistance systems.
Vision-based localization is a key enabler for utilizing and creating the maps. Our group is responsible for aligning the map with the world, and reporting detected changes during the drive. Our technology is safety-critical, and the code has strict time and memory constraints as it operates in real time.
What will your job look like:
Develop and optimize computer vision and deep learning algorithms to accelerate data generation and labeling workflows for autonomous driving
Apply both classical computer vision techniques and modern deep learning methods to solve large-scale data challenges
Collaborate closely with development and annotation teams to enhance automation and ensure high-quality data
Own the entire lifecycle from prototyping to scalable deployment within internal pipelines and tools
Develop tools to evaluate and analyze algorithm performance, robustness, and operational efficiency.

Alice is building its internal AI infrastructure layer from the ground up. We have real agents running in production, a growing base of employees using AI in their daily work, and a clear architectural direction. What we don't have yet is a dedicated engineer to own it. You'll be the first. Your job is to close the gap between "working prototype" and "production platform" - owning the foundation that hosts our agents, the pipelines that ship them, and the reliability layer (observability, cost controls, audit trails, evals) that makes it safe to run AI at scale in a trust & safety company. This is an infrastructure-first role with deep AI fluency - not a prompt engineer, not a wrapper-framework operator, not a no-code builder. You should be equally comfortable writing a Terraform module, debugging a Kubernetes pod, and tracing an agent's tool-call chain. We don’t operate with a predefined backlog here; you will be responsible for identifying high-impact needs and bringing them to life. The perfect fit for this role has a track record of deploying agentic systems that have held up under real-world usage, balances a focus on infrastructure with a deep concern for user experience, and recognizes that the primary hurdle in AI integration is rarely the model itself. Responsibilities: Platform & Infrastructure
* Architect, build, and run the AWS/Kubernetes platform that hosts Alice's internal AI agents and tools; drive AWS Well-Architected pillars (operational excellence, security, reliability, performance, cost, sustainability).
* Own Infrastructure-as-Code: Terraform modules, standards, and reviews for Bedrock, agent runtimes, vector DBs, and supporting services. AI Systems
* Design and ship production-grade agents and multi-agent pipelines using the Anthropic Agent SDK, Claude Code, AWS Bedrock, and MCP — not wrapper frameworks.
* Own the full agent lifecycle: scoping ? prototyping ? eval ? deploy ? monitor ? iterate.
* Integrate agentic workflows into internal and product systems via APIs, databases, webhooks, Slack, and email. Reliability, Observability, Cost
* Build first-class observability across apps and infra: OpenTelemetry, Prometheus, plus LLM-specific tracing (Langfuse or equivalent), token/cost metrics, and eval pipelines.
* Define SLOs/SLIs and error budgets for AI services - latency, model fallback chains, eval regression gates, agent success rates. Lead incident readiness, response, and post-mortems.
* Drive FinOps: model routing by cost, cache hit rates, batch vs. realtime tradeoffs, budget alarms, per-team chargeback visibility.
* Implement guardrails: prompt-injection defenses, PII redaction, model allowlists, human-in-the-loop checkpoints, audit trails. Org Impact
* Identify high-leverage workflows across the organization and translate them into scalable agentic automations.
* Partner with R&D, Delivery, security, and external vendors to deliver platform capabilities.

About Alice:
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact—whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

Hybrid:
Yes

Required Data Engineer
The pioneer in AI-powered sports content technology, empowers more than 460 clients world-wide to connect with their fans through AI-tailored sports content experiences. Our platform automates the creation, management and distribution of content, enabling sports rights holders to expand reach, grow fan bases, and unlock revenue opportunities across digital platforms.
Why us:
Youll work in an awesome environment alongside some of the most innovative people in the industry, using cutting-edge technologies and tools (video editing, Gen AI, data, etc.). You have the opportunity to directly influence the products and tools used by our clients, including sports giants such as the NBA, Bundesliga, LaLiga, ESPN - and thats just the beginning of what we have to offer! Join us and be a part of the best team in tech as we Fuel the Fandom worldwide.
What youll do:
Design and develop scalable, efficient services, APIs, and automations as founder of this area
Build data pipelines, ETL processes, and integrations that power products and internal tools
Own features end-to-end: design, implementation, CI/CD, monitoring, and observability
Develop and optimize data models, ensuring high-performance data infrastructure
Build and schedule workflows with python orchestrator and integrate with data platforms
Optimize performance, reliability, and security across services
Write high-quality, maintainable code in a rich data environment
Collaborate with Product, R&D, Operations, and Business to deliver impactful data-driven solutions
Work with SAAS environments and containerized deployments.

We are looking for a talented and experienced Senior C ++ Developer to join our innovative development team. In this role, you will design, develop, and optimize the core infrastructure of our cutting-edge products. You will be instrumental in ensuring high performance, reliability, and scalability, while collaborating with team members and stakeholders to shape the evolution of our product suite.