דרושים » תוכנה » מפתח /ת ML יישומי - ML משולב במערכות - לא מחקר, יישום

Required Senior Machine Learning Platform Engineer
What will you do?
As a Senior ML Platform Engineer, you will design and build core backend systems and ML infrastructure that power research, training, and online inference at scale.
You will work at the intersection of backend engineering, data engineering, and machine learning, partnering closely with data scientists to transform research needs into production-grade platform capabilities with real business impact.
Key Responsibilities
Design, build, and own backend systems that support ML workflows, data processing, and core platform services
Architect and optimize systems for performance, scalability, reliability, and cost efficiency in production ML environments
Collaborate closely with data scientists to implement, iterate, and evolve ML platform capabilities
Build and maintain large-scale data ingestion, processing, and training pipelines
Ensure systems are production-ready through testing, observability, monitoring, and performance optimization
Investigate and resolve infrastructure and performance issues affecting critical ML and business KPIs.

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.

Were looking for a Senior AI/ML Architect to help shape how our customers implement AI. In this role, youll design scalable, end-to-end ML solutions, from classical models to generative AI, and guide customers from concept to production. Youll work closely with C-level leaders, data scientists, and engineers to deliver impactful, real-world AI outcomes.
📍 Work location - hybrid from Tel Aviv
If you are interested in this opportunity, please submit your CV in English.
Responsibilities
Design and deliver end-to-end ML solutions, including classical ML, deep learning, and generative AI
Create technical proposals, reference architectures, and deployment plans
Lead workshops, POCs, and architecture reviews with startup customers
Translate business needs into scalable ML systems
Implement distributed training workflows and LLM serving infrastructure
Build MLOps pipelines for tracking, versioning, and monitoring
Provide technical guidance to customers and internal teams
Stay up to date on ML trends and contribute to internal enablement
Frequent travels - locally (on-demand to meet with customers and partners and attend local events) and abroad (at least once a quarter).

It starts with you - ML Engineering Team Lead responsible for leading a team delivering end-to-end ML systems. This role combines people management with ownership of model training, evaluation, deployment, and ML platform standards
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
Lead and mentor ML Engineers delivering production ML systems.
Own technical direction for model architectures, training pipelines, and evals.
Review designs and code; ensure scalability, reliability, and data quality.
Define standards for experimentation, reproducibility, and monitoring.
Partner with product and platform teams on roadmap and prioritization.
Balance hands-on technical contribution with people management.
Build a culture of measurement, rigor, and continuous improvement.