דרושים » תוכנה » Experienced Software & MLOps Developer for Route Semantics

Required Experienced Machine Learning Software Engineer - PUMA
As a Machine Learning Software Engineer, you will join our AI Engineering group:
Our goal is to deliver a modern infrastructure and solutions to enhance our algorithm development life cycle and shorten our delivery times.
We are an independent group, consisting of excellent and experienced engineers with diverse skills in software, infrastructure and algorithms.
We strive to implement a DevOps culture allowing our engineers to easily collaborate on large-scale products. We develop products that enable the research and deployment of state-of-the-art algorithms.
It develops the tools and sets the standards that allow great research.
What will your job look like:
Work closely with algorithm developers and infrastructure engineers to design, develop and automate the software that supports distributed training of deep learning algorithms.
Set the standard for developing reliable software, allowing reproducible research at scale.
Build and improve data pipelines to make the development process for deep learning algorithms more efficient.
Develop products that help algorithm developers test their models on large-scale real-world data. Our tech stack includes: Pandas, Polars, PyTorch, Kubernetes, Argo-Workflows, Spark, AWS.

We are seeking a highly skilled Machine Learning Engineer to enable a large team of deep learning engineers, data scientists and computational biologists.

We are looking for a driven candidate to expand our model observability software, lead the optimization of GPU infrastructure, and ensure efficient collaboration with data engineering. The ideal candidate will have development skills in python, deep expertise in cloud infrastructure, high-performance computing, and AI workload optimization. This role involves designing robust GPU management systems, automating model performance metrics, and supporting researchers with the tools they need to train and deploy machine learning models effectively.

Location: Ramat Gan, Israel (hybrid role)

What will you do?

GPU Infrastructure Optimization:
Design and implement strategies to maximize the efficient utilization of GPU resources across the organization.
Develop tools and processes for GPU allocation, workload management, and performance monitoring in alignment with selected infrastructure tools.
Monitor and fine-tune GPU performance to ensure optimal throughput for machine learning workloads.
Model Observability:
Build and maintain a robust system for automated reporting of key model performance metrics.
Integrate with diverse data sources to create customizable dashboards for monitoring performance across datasets.
Set up anomaly detection systems and alerts to ensure timely identification of performance degradation.
Enhance the existing benchmarking suite for seamless evaluation of datasets in federated data lakes.
Collaboration and Support:
Partner with machine learning scientists, data engineers, and DevOps teams to enable researchers to efficiently train and deploy models.
Provide technical guidance and support for effectively utilizing available infrastructure and tools.
Technology Research:
Stay updated with the latest advancements in GPU technologies, ML infrastructure best practices, and model performance metrics.
Evaluate and recommend new tools, technologies, and approaches to enhance the efficiency of the ML enablement platform.
Git and Code Ownership:
Implement best practices for Git workflows, code versioning, and safe release processes.
Foster a culture of high-quality, collaborative development within the engineering team.

We are looking for a highly skilled and motivated Machine Learning Engineer to join our Data Science Engineering team. The ideal candidate will have a deep understanding of machine learning infrastructure and solutions, enabling data scientists and engineers to efficiently develop, deploy, and maintain models in production by delivering a scalable and high-quality solution. This role requires a combination of technical expertise, strong problem-solving skills, and the ability to collaborate effectively with cross-functional teams.

What Youll Do:
Develop and maintain machine learning infrastructure and solutions to support the efficient development, deployment, and maintenance of models in production.
Collaborate with data scientists, engineers, and subject matter experts to deliver high-quality, production-ready machine learning models.
Design and implement software architecture for machine learning solutions.
Optimize model deployment and serving using industry best practices with CPU, GPU, and external APIs.
Work with containerized environments such as Kubernetes and Docker.
Utilize AWS for cloud-based machine learning solutions.
Conduct unit testing and ensure continuous integration and continuous deployment (CI/CD) practices are followed.
Collaborate with the DevOps team to establish best practices for MLOps.
Work in a production environment and closely with data science teams to ensure seamless model deployment and maintenance.
Stay updated with the latest advancements in machine learning and related technologies.

We are seeking a highly motivated and experienced LLM/ML Agentic AI Researcher to lead the technical development of our agentic AI interpretation framework. This hands-on role involves designing, building, and optimizing AI agents that interpret complex biological data and generate coherent, insightful narratives explaining therapeutic mechanisms.

You will be at the forefront of developing a sophisticated scientific reasoning system that leverages Large Language Models (LLMs) to provide structured, biologically-grounded explanations. Collaborating closely with immunologists, machine learning researchers, and technical leadership, you'll shape how we derive insights at a systems level, pushing the boundaries of AI in biology.

Location: Ramat Gan, Israel (hybrid model)

What will you do?
Architect & Develop: Design, implement, and iterate on core AI agents using LLMs and Chain-of-Thought reasoning for creating a scientific reasoning system.
Integrate Biological Knowledge: Effectively access and incorporate biological databases (internal and external databases as tools) ensuring model interpretations are rooted in real biological mechanisms.
Solve Complex NLP Challenges: Innovate solutions to synthesize multiple information sources into cohesive, scientifically robust narratives. Structure advanced prompts and refine LLM interactions to achieve narrative coherence and clarity.
Robustness & Testing: Produce clean, well-documented, maintainable Python code with clear testing protocols, ensuring reliability and accuracy of the agent outputs.
Stay Ahead of Trends: Continuously explore emerging LLM technologies, APIs, and best practices to enhance our interpretation framework.
Effective Collaboration & Communication: Clearly articulate technical concepts to both technical and non-technical stakeholders, effectively integrate feedback from immunologists and researchers, and iterate rapidly to refine the interpretation framework.

We are now hiring our first Prompt Engineer to join the Product team and lead the development of GenAI-powered experiences. Youll work across multiple product lines to design and implement production-grade prompt architectures, embed AI agents into the user journey, and scale experimentation with cutting-edge LLM tools.

Product Team Member Prompt Engineer

This role blends hands-on AI development with product integration strategy. Youll collaborate with product managers and engineers to design, evaluate, and iterate on generative AI solutions including multi-step prompt chains, agent workflows, and LLM selection frameworks. Your work will shape how personalization evolves across the platform.

All About the Role

Design and refine prompt architectures (e.g., few-shot, chain-of-thought, tool use) that drive our LLM-powered features

Select and evaluate models, frameworks, and orchestration tools (e.g., OpenAI, Claude, LangChain, RAG)

Collaborate with engineers to implement scalable agent-based workflows and integrate GenAI into the product

Partner with Product Managers to define user-facing use cases and influence product direction with AI capabilities

Develop internal tooling and evaluation frameworks to test prompt quality, model behavior, and UX alignment

Act as a thought leader on prompt best practices, model safety, and responsible AI design across product squads