דרושים » תוכנה » Deep Learning Engineer

We are looking for a Senior ML Algorithm Engineer to lead the development and optimization of machine learning models for challenging real-world problems.
In this role, you will work hands-on across the full model-development lifecycle: understanding the task, designing and adapting algorithms, building effective training strategies, analyzing data and failure modes, and improving model quality and efficiency through rigorous experimentation.
This is an algorithm-focused role for someone who enjoys getting deeply involved in the details of model training and optimization. You will not simply operate an existing training infrastructure-you will investigate open-ended problems and develop practical solutions involving model architecture, data and sample selection, training objectives, optimization methods, and evaluation
What will your job look like:
Work on the semantics of road objects, using harvested tabular data as the primary input to our algorithms - turning large-scale, real-world observations into models that capture object meaning, attributes, and behavior on the road network.
Design, implement, and optimize machine learning and deep learning algorithms for these semantic tasks, from architecture and training objectives through evaluation and efficiency.
Develop and improve end-to-end model training pipelines, from data preparation and sampling of harvested tabular datasets through training and evaluation.
Investigate model behavior, identify failure modes, and drive targeted algorithmic improvements.
Develop effective strategies for data selection, dataset composition, sampling, augmentation, loss design, and training schedules.
Lead the investigation of complex algorithmic challenges, uncover patterns in data and model behavior, and translate insights into measurable improvements.

We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying our AI models. You will own and evolve our 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.
This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our 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.

Required Senior ML Data Engineer
About the team:
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

As an MLOps Team Leader, you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.

What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power our AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

We are looking for a DevOps Engineer to own and scale the infrastructure behind our AI systems.

What you'll do
Own cluster reliability, availability, monitoring, alerting, capacity planning, and incident response.

Configure, maintain, and troubleshoot GPU servers, networking equipment, switches, InfiniBand fabrics, storage, and related infrastructure.

Deploy, maintain, and improve infrastructure around Slurm, Kubernetes, and GPU workload orchestration.

Manage and improve our cloud environment, including compute, networking, storage, IAM, security groups, cost visibility, and production infrastructure.

Build, maintain, and improve CI/CD pipelines.

Work closely with engineers to understand infrastructure bottlenecks and improve development, training, inference, and deployment velocity.

Contribute to security best practices across cloud, network, CI/CD, access control, secrets management, and production systems.

We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership.
As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level.
You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers.
Responsibilities
Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies.
Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph.
System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks.
Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment.

We are hiring a Software Engineer to help revolutionize the world of observability. Using cutting-edge AI-driven technology, we are redefining how organizations monitor, analyze, and optimize their systems-making observability more intelligent, efficient, and accessible than ever before. As we scale, we seek top talent to push the boundaries of innovation and shape the future of AI-powered observability. If you're passionate about solving complex challenges and building game-changing technology, join us in transforming how the world understands and interacts with data.
Responsibilities:
End-to-end development and ownership of products and features, from design to scalable and predictable production behavior
Solve diverse and complex problems in a high-scale, cloud-native environment
Collaborate with other engineers and product managers to improve product functionality, scalability, and performance
Design, develop, and maintain robust, secure, and efficient software solutions
Ensure high system reliability by implementing best practices in monitoring, observability, and automation
Review code, architecture, and data to identify and troubleshoot technical and performance issues
Work with AI/ML teams to integrate AI capabilities, including model monitoring, evaluation, and fine-tuning.

We are looking for a Product Engineer to own and ship the products that put this capability in the hands of engineers, data scientists, and researchers.

What you'll do
Own product features end-to-end: scope, build, ship, measure, and iterate - across CLI, IDE integrations, web surfaces, and the AI pipeline behind them.

Talk directly to users - engineers, data scientists, ML practitioners - and let their feedback shape what you build next.

Define what AI-powered code optimization and verification feels like as a developer experience: the interaction model, the feedback loops, the trust signals. There is no established playbook; you will create the conventions.

Craft interfaces that feel fast, focused, and effortless for technical users who have zero patience for friction.

Collaborate with researchers to translate model breakthroughs into polished, reliable tooling that developers can depend on in production.

Build evaluation infrastructure and product metrics that measure whether the tools are actually getting better.

Work directly with the founding team on product strategy and roadmap.