לוח משרות דרושים פיתוח אלגוריתמים בתל אביב יפו, לחיפוש זה נמצאו 15 משרות

We are looking for a Senior Data Scientist to lead the design and implementation of next-generation AI solutions for Lifecycle Marketing.
As a senior member of our team, you will shape methodology, guide product direction under uncertainty, and represent in high-stakes conversations with customers. Youll combine deep expertise in causal inference, reinforcement learning, and experimentation with strong communication skills to drive impact across the company and with our clients.
Responsibilities:
Own the design and methodology of data science solutions for lifecycle marketing, with emphasis on causal inference, uplift modeling, and reinforcement learning.
Lead the development of experimentation frameworks (A/B/n tests, variance reduction, policy evaluation).
Research, prototype, and productionize multi-armed bandits and contextual RL approaches for personalization and timing optimization.
Take a strong, opinionated role in brainstorming sessions, guiding decision-making in areas of high uncertainty.
Represent Voyantis in front of customers, translating complex modeling decisions into clear business value.
Mentor junior and mid-level team members, raising the technical bar across the team.
Collaborate closely with product managers, engineers, and analysts to align data science strategy with business goals.

As a quantum researcher, your main role will be to develop algorithmic and error-reduction tools to help our partners and customers achieve quantum advantage for their applications. You will develop our companys error reduction methods, including mitigation and correction, and apply them to different use cases by optimizing existing methods, developing new algorithms, and adapting these tools to the realities of running on quantum hardware. This includes taking into account application requirements, noise characteristics, device constraints, and performance on real quantum devices.
Responsibilities
Developing new error reduction methods, including mitigation and correction, for quantum algorithms.
Optimizing error reduction methods for different algorithms, applications, and hardware settings.
Developing new quantum algorithms to enhance performance on real quantum hardware with error mitigation.
Conducting research on applications of quantum computing with our companys partners, including implementing and running proof-of-concept demonstrations of these applications on real quantum hardware. 

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.
We are looking for an experienced Senior Algorithms Developer to join our team and help design, build, and scale the systems that power our products. In this role, you will work on high-impact projects that combine cutting-edge technology, complex distributed systems, and challenging algorithmic problems in transportation, routing, and navigation. You will have the opportunity to own features end-to-end, make key technical decisions, and shape the architecture and reliability of large-scale systems.
What Youll Do:
Architect, build, and scale robust, high-performance systems from initial design through deployment to production.
Own features end-to-end, from concept through development, testing, deployment, and maintenance, with strong accountability.
Develop features and APIs for concurrent applications in Golang.
Implement and improve geometric and graph algorithms to support core product
functionality.
Maintain Python and Java server applications, ensuring reliability and stability.
Write production-grade code that is efficient, reliable, maintainable, and optimized for performance.
Optimize application performance, uptime, and scalability, while maintaining high standards of code quality and thoughtful application design.
Run benchmarks and write analysis scripts for geographical data.

we are looking for a Algorithm Researcher.
Develop the optimization and computational-geometry algorithms at the core of : for researchers who love hard, non-convex problems.

We are looking for a Senior Algorithm Developer with experience in NLP and LLMs, who is highly motivated, collaborative, and results-driven individual who can work both independently and as part of a multidisciplinary team. The ideal candidate has a strong can-do attitude, excellent problem-solving skills, and a passion for leveraging AI to solve meaningful challenges in text analysis in the medical domain.

As part of the AI team you will:

Design, develop, implement, and evaluate robust, scalable AI algorithms and agentic workflows tailored to real-world challenges in clinical settings.

Prepare, process, and analyze complex medical data to support algorithm development and validation.

Ensure the reliability, performance, and accuracy of algorithms in production, including troubleshooting, testing, monitoring, and continuous improvement.

Collaborate closely with Product Managers and Clinical Specialists to translate clinical and business needs into data-driven solutions.

Research, develop and integrate cutting-edge text based alogirthms on medical text.

Apply NLP and Large Language Models (LLMs) to extract insights from medical texts and develop intelligent agents for optimized clinical decision support.

Stay up to date with cutting-edge advancements in GenAI, LLMs, and Deep Learning.

Organize and actively participate in seminars, book clubs, and conferences.

Main Technical Stack:

NLP/LLMs: Pydantic, Hugging Face Transformers, spaCy, Amazon Bedrock

Infrastructure: AWS, Docker

Programming Languages: Python

Deep Learning Frameworks: TensorFlow, Keras, Pytorch

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.

We're currently seeking a Data Researcher to join our Data Team and help proactively guide our business growth through the provision of strategic perspective, market insight, and experience with analytical tools and techniques. You will work closely with product managers, medical analysts, and the engineering team and bring your expert analytical ability to provide key insights, recognize key opportunities, and drive strategic initiatives forward.

Responsibilities
Build and maintain data pipelines and dashboards to improve data visibility and reporting in production environments
Implement AI-powered workflows for internal and external uses
Work with product, medical, and engineering teams to turn data into insights and recommendations.
Conduct exploratory analysis to identify opportunities for new features, model their impact, and drive performance improvements.
Analyze product performance, user behavior, and interactions to showcase product value.
Define, measure, and build monitoring tools for key KPIs.
Visualize insights clearly to improve understanding and decision-making.
Support roadmap planning by identifying trends, gaps, and opportunities.

we are looking for an experienced, independent team player who has great data & computer science skills, a passion for data, and excellent analytical and algorithmic skills.
This role is diverse, encompassing AI/ML, data analysis, and backend engineering.
You will play a crucial role within a mission-critical team responsible for managing the heart and brain of primary products. This involves working on core systems, such as POS underwriting models, models for merchant operations, Fraud investigator AI agents and more.
You will participate in cutting-edge risk management systems that safeguard the loan product's financial operation, as well as operational systems, with a direct impact on its financial success.
Our AI/ML team is part of the R&D group, so you will work closely with engineers and product managers as well.
Key Responsibilities
Research and develop statistical behaviors, study domain-specific data.
Develop state-of-the-art machine learning models end to end, including development, deployment, and continuous improvement. Both in-weight learning and in-context learning, for risk / fraud / operations related projects. This includes integrating models into production services and ensuring compliance with regulatory processes (e.g. providing evidence for production model audits).
Operate backend infrastructure for training and deploying ML models, ensuring optimal performance and reliability.
Develop and maintain Python code for translating ML model outputs into financial decisions.
Analyze 15+ different data sources in order to train models and agents to catch fraudulent patterns.
Conduct analytical research on our models impact on the portfolio.
Strategize and implement changes to enhance portfolio performance.

As a Technical Policy researcher, you'll tackle some of the biggest and most complex questions of AI development and deployment. Your work outlining problems, solutions and principles for governments, frontier labs, and deployers will shape the emerging field of AI security.

Representative projects:

Threat modeling and fleshing out in what specific way could a strong cyber-capable model cause harm.

Taxonomies for dangerous AI capabilities or mitigations.

Policy proposals for models' refusal policy (which prompts models should refuse to follow).

Following up on publications and news on AI policy, AI risks and mitigations, and technical AI research.

Writing academic papers and blog posts detailing our research around evaluation theory or mitigation recommendations.

Publishing your research and/or delivering research to our customers.

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 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.