דרושים » מדעים מדוייקים » Senior Machine Learning Engineer

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.

Design, implement, and train state-of-the-art neural network architectures for robotic applications, including large multimodal models (vision, language, sensor data) and generative models.
Architect and maintain scalable deep learning frameworks for training on massive, real-world robot datasets.
Develop and implement cutting-edge techniques in areas such as imitation learning, reinforcement learning, self-supervised learning, and video-based models.
Own the full machine learning lifecycle: define data collection and labeling strategies, build high-throughput data pipelines, and establish robust evaluation and deployment protocols.
Collaborate closely with software, hardware, and controls teams to integrate and test your models on physical robots.
Leverage and contribute to internal tooling for large-scale data processing, model experimentation, and continuous integration.

Required Researcher
Description
We are a world leader in trading algorithms and trade execution technologies development. Our multi-disciplinary teams have developed a unique and highly successful machine learning algorithmic based HFT platform that delivers excellent results.
Our research is run by an outstanding team of brilliant researchers who love the extreme challenge of beating the markets. We consistently profit in challenging markets that get increasingly efficient.
We have no customers, and no marketing. It is a pure technological and algorithmic challenge. Hence the time-to-market from a research idea until it is used and make profit by our trading machines is very short.
In a world increasingly dominated by learning machines and artificial intelligence, we are especially proud of our humans. Our elite team of exceptional people are the soul of our company, and it is our top priority to provide them with a professionally fulfilling environment that supports healthy work-life balance. Our employees are encouraged to pursue their passions outside of work and we are proud to offer them a variety of opportunities, multiple resources and an agile work environment which promotes their well-being.
Our unique research team is expanding, we are looking for smart and humble team members to join the team and solve complex problems. Our technical challenges include developing new trading strategies, , improving the performance of our algorithms, finding new ways to beat the markets. As an ML researcher you will collaborate with our R&D team to execute live trading strategies. If you are looking to make an impact by putting into practice successful ideas and seeing immediately the outcome, make influence on our financial performance this is a great opportunity.
In this role you will:
Mange and lead independent research applying ML/DL methods to a wide variety of datasets.
Creatively find new trading strategies
Work with other team members to optimize our Algorithms.

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 highly talented Postgraduate Engineer - MSc/PhD to join our Autonomous Systems team and lead the development of a foundation model that will serve as the backbone of our autonomous robot.

Responsibilities:

Process large-scale, multi-modal data from diverse sources.
Design, develop, and build Foundation Models to support perception for autonomous agents.
Build data pipelines, training frameworks, and evaluation methodologies.

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.