דרושים » תוכנה » ‏Sr Machine Learning Scientist‏

This job will design, develop, and implement machine learning models and algorithms to solve complex problems. You will work closely with data scientists, software engineers, and product teams to enhance services through innovative AI/ML solutions. Your role will involve building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences.
Job Description:
Essential Responsibilities:
Develop and optimize machine learning models for various applications.
Preprocess and analyze large datasets to extract meaningful insights.
Deploy ML solutions into production environments using appropriate tools and frameworks.
Collaborate with cross-functional teams to integrate ML models into products and services.
Monitor and evaluate the performance of deployed models.

Required ML Data Engineer
Israel: Tel Aviv/ Hybrid (Israel)
R&D | Full Time | Job Id: 24792
Key Responsibilities
Your Impact & Responsibilities:
As a Data Engineer - AI Technologies, you will be responsible for building and operating the data foundation that enables our LLM and ML research: from ingestion and augmentation, through labeling and quality control, to efficient data delivery for training and evaluation.
You will:
Own data pipelines for LLM training and evaluation
Design, build and maintain scalable pipelines to ingest, transform and serve large-scale text, log, code and semi-structured data from multiple products and internal systems.
Drive data augmentation and synthetic data generation
Implement and operate pipelines for data augmentation (e.g., prompt-based generation, paraphrasing, negative sampling, multi-positive pairs) in close collaboration with ML Research Engineers.
Build tagging, labeling and annotation workflows
Support human-in-the-loop labeling, active learning loops and semi-automated tagging. Work with domain experts to implement tools, schemas and processes for consistent, high-quality annotations.
Ensure data quality, observability and governance
Define and monitor data quality checks (coverage, drift, anomalies, duplicates, PII), manage dataset versions, and maintain clear documentation and lineage for training and evaluation datasets.
Optimize training data flows for efficiency and cost
Design storage layouts and access patterns that reduce training time and cost (e.g., sharding, caching, streaming). Work with ML engineers to make sure the right data arrives at the right place, in the right format.
Build and maintain data infrastructure for LLM workloads
Work with cloud and platform teams to develop robust, production-grade infrastructure: data lakes / warehouses, feature stores, vector stores, and high-throughput data services used by training jobs and offline evaluation.
Collaborate closely with ML Research Engineers and security experts
Translate modeling and security requirements into concrete data tasks: dataset design, splits, sampling strategies, and evaluation data construction for specific security use.

Required Senior ML Research Engineer
Israel: Tel Aviv/ Hybrid
R&D | Full Time | Job Id: 24793
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 -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.

It starts with you - a senior ML engineer responsible for building, training, evaluating, and operating machine learning systems in production. The role focuses on data pipelines, model training, experimentation, evaluation, and scalable deployment.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.

Today, more people than ever are speaking publicly about their mental health. Whether it's ourselves, our friends and family or even public figures, taking care of your behavioral health is no longer a taboo, it's vital, and it's only human.

we are on a mission to help deliver the world's most effective behavioral care through data, measurement, and personalization. Or simply put, we want to give clinicians the support they need to do the important work only they can do.

What is this opportunity?
At our company, we build a behavioral health CareOps automation platform that transforms therapy conversations into structured insights and clinical documentation. Our system uses advanced ML and LLM technologies to improve care quality, support therapists daily workflows, and reduce documentation time by over 50%.

As a Senior ML Infrastructure Engineer, you will design and build the infrastructure that powers our ML and LLM systems in production. You will develop scalable pipelines, systems, and tools that enable data scientists and AI teams to efficiently develop, test, and deploy models.
Working closely with data scientists, engineers, and product teams, you will ensure our ML capabilities are reliable, scalable, and production-ready-helping bring cutting-edge AI to improve mental health care.
This is a unique opportunity to join a startup with a real impact on thousands of peoples wellbeing and mental health, applying cutting-edge AI technologies to solve meaningful human problems.

How will you contribute?
Design and build infrastructure and backend services supporting ML and LLM systems

Develop and maintain ML training and deployment pipelines

Build tooling that enables model experimentation, versioning, and reproducibility

Implement CI/CD pipelines for ML workflows and model deployment

Support LLM deployment, prompt management, and optimization pipelines

Improve reliability, monitoring, and observability of ML systems in production

Collaborate with data scientists to productionize models and research prototypes
Ensure secure and compliant handling of sensitive healthcare data

We are always looking for exceptional talent to join us on the journey!
Your Mission:
As an MLOps Engineer, your mission is to design, build, and operate the platforms that power our machine learning and generative AI products spanning real-time use cases such as large-scale fraud scoring, MCP & agentic workflows support. Youll create reliable CI/CD for models and Agents, robust data/feature pipelines, secure model serving, and comprehensive observability. You will also support our agentic AI ecosystem and Model Context Protocol (MCP) services so that models can safely use tools, data, and actions across.
You will partner closely with Data Scientists, Data/Platform Engineers, Product, and SRE to ensure every model from classic ML to LLM/RAG agents moves from prototype to production with strong reliability, governance, cost efficiency, and measurable business impact.
Responsibilities:
Operate & Develop ML/LLM platforms on Kubernetes + cloud (Azure; AWS/GCP ok) with Docker, Terraform, and other relevant tools
Manage object storage, GPUs, and autoscaling for training & low-latency model serving
Manage cloud environment, networking, service mesh, secrets, and policies to meet PCI-DSS and data-residency requirements
Build end-to-end CI/CD for models/agents/MCP tooling (versioning, tests, approvals)
Deliver real-time fraud/risk scoring & agent signals under strict latency SLOs.
Maintain MCP servers/clients: tool/resource definitions, versioning, quotas, isolation, access controls
Integrate agents with microservices, event streams, and rule engines; provide SLAs, tracing, and on-call runbooks
Measure operational metrics of ML/LLM (latency, throughput, cost, tokens, tool success, safety events)
Enforce governance: RBAC/ABAC, row-level security, encryption, PII/secrets management, audit trails.
Partner with DS on packaging (wheels/conda/containers), feature contracts, and reproducible experiments.
lead incident response and post-mortems.
Drive FinOps: right-sizing, GPU utilization, batching/caching, budget alerts.

We are seeking for a talented AI/ML developer with strong focus on Python expertise and hands-on experience in AI/ML models to join our Data Science group.
The Data Science group plays a pivotal role in driving the execution and delivery of advanced AI and modern Machine Learning solutions, that empower impactful projects to our target sectors.
Key Responsibilities:
Take part in all development aspects, from concept to production, of AI/ML models and LLM-based features (agents development)
Stay up to date with state-of-the-art research in the field of LLM and apply relevant advancements to improve our solutions
Mentor new team members and share your expertise to strengthen the teams collective knowledge

Were looking for a Senior Data Scientist to take full ownership of high-impact ML problems within our DSP platform.
You will work on core bidding, budget optimization, and prediction systems that operate at massive scale and strict latency constraints. This role combines deep modeling expertise, experimentation rigor, and strong product intuition.
You will be expected not only to build models - but to define problems, challenge assumptions, and drive measurable business impact.
What Youll Do:
Own end-to-end ML projects: problem definition → research → modeling → offline validation → production deployment → online A/B testing → impact analysis.
Develop and improve models for: Bid optimization , Conversion rate / pLTV prediction, Budget pacing and allocation, Auction dynamics & win-rate modeling.
Analyze large-scale, high-dimensional auction and user-level datasets to extract actionable insights.
Design robust feature engineering pipelines across behavioral, contextual, and advertiser-level signals.
Improve model performance under real-time constraints (low latency, high throughput).
Lead experimentation design and statistical validation of online tests.
Collaborate closely with engineering and product to translate research into scalable production systems.

seeking an experienced and visionary Head of Research to lead, shape, and advance the future of AI innovation across the organization. In this role, you will own the research strategy end-to-end - from long-term roadmap design to hands-on exploration, and guide a multidisciplinary team of NLP research engineers and linguists toward breakthroughs that elevate our core medical coding engine.

You will play a key role in the company, balancing exploratory research with high-impact product delivery. You will partner with Product, Engineering, and executive stakeholders to identify strategic opportunities, drive prioritization, and translate research capabilities into real-world outcomes at scale.

Responsibilities:

Manage and mentor a multidisciplinary research team while driving both technical excellence and personal growth.
Define the long-term research roadmap aligned with product and business objectives.
Lead the full research lifecycle, from ideation and feasibility studies to experimentation, evaluation methods, and production-ready delivery.
Evaluate and introduce new research capabilities and emerging trends such as LLM-based reasoning, semantic/graph modeling, hybrid rule-learning, and other frontier techniques.
Continuously track scientific trends to maintain a state-of-the-art research practice.
Collaborate closely with Product and Engineering teams to translate domain challenges into algorithmic solutions with measurable product impact.
Represent the Research function at leadership forums, communicate priorities and outcomes, and align stakeholders around research strategies and milestones.

We are looking for a Senior Engineer with a data engineering background to join our growing ML Platform team. This is a great opportunity, whether you have experience with ML and are looking for a ML focused product or are an experienced Data Engineer looking to enter the world of ML. Together well provide tools to develop more effective models, get them into production faster, and ensure that they continue to perform well over time.
ML is central to our work. It enables us to process billions of $ worth e-commerce transactions, make decisions in real time, identify fraud rings, and quickly detect new attack methods. Precision is crucial - bad decisions by our models cost us directly and put money into the pockets of fraudsters.
Our adoption by merchants around the world provides us with billions of fresh data points each day. Our team of data scientists, analysts, and cyber intelligence specialists continually identify new signals, engineer new features, and research new models. But as the volume of data and the number and complexity of models grows, so do the engineering challenges.
If this kind of working environment sounds exciting to you, if you understand that Engineering is about building the most effective and elegant solution within a given set of constraints - consider applying for this position.
Why should you join us?
Youll be part of a highly proficient engineering team that is a focal point for all ML engineering activity, striving to constantly bring innovation and leverage ML capabilities across all company teams and products.
This role presents a unique opportunity to enter the ML domain. For those already experienced in ML infrastructure, it offers the chance to grow within a team that specializes in high-scale, Big Data and ML systems.
What you will be doing:
Designing, building, and maintaining the ML infrastructure that allows our models to make billions of real-time decisions every year.
Building a platform that enables managing a full ML model lifecycle - from researching to training, deploying, and serving predictions in real-time.
Building distributed data processing pipelines to support model development.
Acting as a consultant to researchers, data scientists, and expert analysts and enabling them to research new models faster and with greater precision by providing cutting-edge tooling.
Expanding our ML infrastructure to make it scalable, quick, and efficient to bring diverse models to production and to monitor their performance and drift over time.
Expanding the pool of internal customers able to use ML. Work with them to understand their needs and help them make the most of the infrastructure that well provide.
Acting as an advocate for MLOps, continually improving our processes, and raising our standards.