דרושים » מדעים מדוייקים » deep learning researcher

Were looking for talented Senior Algorithm Engineer to help us develop innovative technologies for cancer diagnosis.
In this role, youll work on huge, unique datasets of high-resolution pathology images from leading institutes worldwide, develop and optimize algorithmic solutions for complex problems using cutting-edge technology, integrate your algorithms into our products and receive feedback from clinicians using them in live clinical settings, and work closely with our multi-disciplinary team of Machine Learning experts, experienced software developers and medical professionals.

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 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 youll 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 seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.

Responsibilities:

Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.

As a Software Engineer at Irregular, you will take ownership of designing, building, and scaling the production systems that power our evaluation and security platform for frontier AI models.

Your work will focus on creating robust, resilient, and high-performance infrastructure-whether thats distributed pipelines, backend services, or tooling that supports our research teams.

This role is engineering-first with a strong research and cyber component. You will develop systems that must run reliably in production, integrate with external partners, and support large-scale data, experiments, and automated evaluations. Youll drive architectural decisions, lead technical implementations, and shape how our platform evolves.

Representative Responsibilities:

Architecting and scaling production-grade systems and workflows.

Building backend services, APIs, and monitoring tools for large-scale model evaluations.

Designing infrastructure that supports research experiments at scale.

Implementing agent frameworks in production environments

Designing and building challenges that measure a models ability to evade discovery, allowing us to see if models can operate on remote systems while avoiding detection by common defensive security tools.

We are looking for a Software Engineer to join our core Algorithm team as an dedicated data-core / platform engineer. You will support the algorithm engineers developing detection algorithms to design and build the infrastructure they run on: the object-detection (OD) pipeline, its orchestration and deployment, its data and model catalog, and the benchmark and labeling systems that drive model improvement. This is a high-autonomy role with real, end-to-end ownership of production systems from day one.

The technologies listed in this description are examples of our day-to-day - not a rigid checklist. We care far more about how you think, how you plan, how you debug, and how fast you learn than about which specific tools you have already used. If you love the craft of making things work and want to go deep, you can learn the rest here.

In the AI era, being a strong engineer means more than writing great code. It means operating as an architect who directs AI agents - designing the solution with clarity, then guiding them to execute it at a level and speed that wasn't possible before. We are building a culture where this is the norm, and we're looking for someone who is excited to work and grow in that direction.



What You'll Do

Take on hard, open-ended infrastructure challenges and make them work - designing, building, decoupling, and hardening the systems behind our object-detection pipeline, from data and model management to benchmarking, so everything runs reliably at scale.
Architect and build the backbone of the OD pipeline - orchestration (Airflow on Kubernetes), data plumbing (S3 / PostGIS / SQS), CI/CD, and deployment across multiple environments - designing clean interfaces and data contracts the algorithm team can build on with confidence.
Debug across the whole stack, wherever the problem leads - a stuck DAG, a flaky pipeline stage, a slow query, a GPU/driver mismatch - and turn one-off firefights into lasting fixes and better observability.
Own the data and model lifecycle: versioned datasets and model weights with clear provenance, and the labeling → export → retraining loop that keeps the models improving.
Learn fast and go deep. Pick up new tools and new layers of the stack as the work requires, and raise the team's engineering and operational standards as you go.
Partner closely with algorithm engineers and the data-collection / labeling operations team to turn research prototypes into robust, scalable production systems.
Integrate AI tools into your workflow and grow into operating as an architect who directs AI agents - designing the solution, then guiding them to build it.

As an AI Researcher on Research group, youll research, develop and productionize LLMs powered applications and AI-agents. Youll partner closely with Product and Engineering to turn open-ended data-security challenges into measurable experiments and shipped features. Youll own the end-to-end lifecycle - from problem framing and data strategy to evaluation, deployment, and ongoing monitoring - helping customers discover, protect, and govern their data at scale.



What Youll Do

Responsible for the end-to-end research process. This includes identifying problems, preparing data, tuning and developing models, deploying to production, and analyzing outcomes.
You will be a hands-on domain leader, laying the foundations of our data science workflows and algorithms. This is an excellent opportunity to work with endless amounts of data and creatively generate insights that will increase the ability to classify tons of data.
Develop, evaluate, and maintain deep learning and NLP solutions to enhance core capabilities in sensitive data classification.
Design and architect production-grade agentic workflows. Establish rigorous evaluation pipelines to benchmark agent accuracy, latency, and cost, ensuring reliable, scalable solutions for real-world customer problems
Innovation and creative thinking are the keys! Implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification.
Join a full stack AI group, including research engineering, MLEs, data operations, and security researchers. You will accelerate the path from research to production, ensuring results are both quick and precise.

We are looking for an exceptional Deep Learning Engineer to join our core autonomy team. In this role, you will be responsible for building the AI-driven "brain" of our autonomous driving technology. Your focus will be on designing, training, and deploying state-of-the-art Deep Learning models that handle high-level prediction and driving policy. You will solve complex, real-world challenges by leveraging advanced neural network architectures to optimize decision-making under high uncertainty.

Responsibilities:
Model Training & Architecture: Design, train, and optimize cutting-edge Deep Learning models for prediction and driving policy.
End-to-End DL Development: Take ownership of the full DL lifecycle, from data curation and feature engineering to training, evaluation, and deployment.
Scalable Infrastructure: Develop robust pipelines for training and evaluating models on massive, real-world driving datasets.
Production Deployment: Optimize and integrate deep learning models to run efficiently in real-time within the core autonomy software stack.
Full-time availability at our Tel Aviv HQ.