דרושים » תוכנה » Senior Applied AI Researcher

We are looking for a passionate Senior Data Scientist to join our all-star team. This is an amazing opportunity to join a multi-disciplinary A-team while working in a fast-paced, results, marketing and data-oriented environment. If you are experienced but still hungry to learn and impact - wed love to have you on our team!



What Youll Do

Devise, initiate, manage, and execute cutting-edge exploration and exploitation projects.
Apply your knowledge of Data Science, Machine Learning, and AI-driven algorithms to create scalable data solutions.
Analyze extremely large (petabytes) and diverse datasets to solve complex problems, and develop automated, intelligent systems to address them.
Design, build, and deploy AI/ML models (including predictive models, recommendation systems, and generative AI solutions) to drive business impact.
Leverage modern AI tools and frameworks to accelerate research and production workflows.
Proactively seek growth opportunities aligned with core KPIs.
Enable real-time and near real-time data insights - instant data gratification guaranteed.

As a Senior Machine Learning Engineer, you will work closely with top notch engineers and data scientists to design, develop, evaluate and deploy Gen AI-powered solutions for scalable, customer-facing applications. Your work will focus on building and applying state-of-the-art agentic capabilities to drive business impact and improve efficiency.



Key Job Responsibilities and Duties:

Design, develop, and deploy high-quality, performant, and efficient Generative AI-powered solutions and agentic systems into production environments.

Evaluate and define optimal architectural solutions by considering emerging technologies, business needs, and technical requirements for latency, throughput, and scale.

Own services end-to-end, including implementing robust monitoring and maintenance strategies to ensure application and ML health, quality, and performance.

Write and maintain clean, scalable, and well-tested production code, ensuring reproducibility and seamless integration via CI/CD pipelines.

Pioneer and promote best practices and the adoption of cutting-edge technology in GenAI application development.

Collaborate effectively with Product Managers, Data Scientists, and Analysts to understand business requirements and translate them into technical ML and agentic solutions.

Provide technical guidance and mentorship to other engineers, contributing to the team's overall technical development.

At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we pioneered the world's first fully automated suite of AI-powered vehicle inspection systems, combining computer vision, machine learning, and generative AI.
With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone.
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 UVeye’s 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 you’ll 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 looking for a AI Applied Scientist - Insights & Recommendations.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.

We are seeking a Machine Learning Engineer to join the team that builds the predictive intelligence powering the Hello Heart app. You will own the ML models behind user engagement, cardiovascular risk stratification, and personalized health recommendations - the systems that determine what users see, when they're nudged, and how their health trajectories are shaped.

This role demands both statistical depth and engineering proficiency - you will be expected to take models from research through to deployment, write code built for production, and use AI coding assistants fluently as part of how you get work done.

Responsibilities
Lead end-to-end development of predictive ML models. From data exploration and feature engineering through training, validation, deployment, and ongoing monitoring across engagement and clinical risk domains.
Apply strong statistical foundations to model design, feature selection, uncertainty quantification, and interpretation of results
Write production-grade Python code that is clean, tested, and built for maintainability and scale.
Use AI coding assistants to accelerate development, code review, and documentation without sacrificing quality or rigor.
Partner with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
Research and implement cutting-edge ML techniques spanning supervised and unsupervised learning, causal inference, deep learning, and reinforcement learning to tackle complex healthcare challenges.
Contribute to MLOps infrastructure: model serving, versioning, evaluation pipelines, and monitoring.
Design and interpret A/B tests and other experimental methodologies to measure the impact of models, features, and interventions.

We are seeking a talented and motivated Data Scientist for a temporary position to support our growing data, analytics, and AI automation needs. This role focuses on turning large volumes of data into models, insights, and intelligent automation - combining classic data science (statistical analysis, feature engineering, machine learning) with the emerging Agentic AI stack (LLMs, MCP, agent orchestration). You will work closely with data engineers and internal teams to prototype and productionise models, build LLM-powered agents and workflows, and support the integration of AI capabilities across the organization.

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.


Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.

Senior AI Research Engineers at Simply develop algorithmic solutions that power new innovative features and continuously improve existing ones, with a strong emphasis on training AI / ML models as a core part of the role. The work spans a wide range of domains - from training generative AI and LLM-based models to create musical content at scale for Simply Piano, through building deep learning computer vision models that enhance user drawings in Simply Draw, to developing real-time audio ML models for music transcription that elevate feedback.

You will take full end-to-end ownership of cutting-edge algorithms and AI models - leading the process from ideation and research, through development, to production-grade implementation and engineering within products. This includes not only designing and building AI models, but also ensuring robust, efficient, and scalable integration into the app - directly shaping the experience of millions of users.

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions that run on real hardware in production labs.

You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks that make quantum control more autonomous, more sample-efficient, and more robust to drift. This position offers unprecedented exposure to diverse qubit types and quantum architectures, with a tight feedback loop between your models and the systems they steer, and the opportunity to deliver groundbreaking ML-driven solutions to the labs and companies defining the next generation of quantum systems.

Responsibilities:

Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement, to be deployed on real hardware.
Develop real-time parameter steering for calibration during QEC and between circuits.
Develop and maintain agentic frameworks for autonomous system control and calibration.
Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum Machines control stack, including QUA, Qualibrate, and the OPX1000.
Work directly with customers and partner labs to deploy, validate, and iterate on ML solutions in real experimental environments.
Collaborate cross-functionally with product, R&D, and hardware teams, contributing to internal libraries, customer-facing SDKs, and training materials.

As a Machine Learning Scientist II, you will work within a cross-functional team of engineers and product managers to develop, evaluate, and deploy GenAI-powered solutions for scalable, customer-facing applications. Your work will focus on implementing agentic capabilities, contributing to evaluation frameworks, and delivering measurable business impact through data-driven experimentation.


Key Job Responsibilities and Duties:

Contribute to the design and development of end-to-end agentic systems, ensuring code quality and efficiency in production.

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Adhere to and promote established best practices in GenAI application development within the team.

Collaborate actively with team members, participating in code reviews, sharing knowledge, and contributing to a positive team environment.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.