דרושים » תוכנה » Senior Software Engineer (Platform)

We are looking for a talented Software Engineer to join a team building an internal, AI-powered platform designed to significantly improve product quality by identifying edge cases and long-tail scenarios in autonomous driving systems.
Using state-of-the-art Vision and Language models, the team is rethinking how training, validation, and stress-testing are done.
This is a highly impactful engineering role focused on building innovative internal products end-to-end- tools developed from scratch that are designed to become part of the companys daily workflows and directly influence core algorithmic and product decisions.
If youre passionate about deep learning-driven products, large-scale experimentation, and owning complex systems from design to production- this is the place for you.
What will your job look like:
Take part in building an AI search platform from scratch, and owning it end to end in a production environment.
Design and implement full-stack solutions, including backend services, frontend interfaces, and database management.
Work with cloud-based systems, containers, and modern software infrastructure to support performance, scale, and reliability.
Collaborate closely with algorithm developers and internal users to expose complex capabilities in a simple, usable way.

We are hiring a Senior Software Developer to lead the development of our internal malware research platform. This is a senior, hands-on role with end-to-end ownership of development and delivery. You'll be the technical authority - setting code-quality standards, making the architecture calls, and mentoring the developer team.
What makes this role different is who you build for. Our users are our malware researchers, and they use the tool every day. Your job is to sit beside them, learn how they actually work, surface the heuristics and edge cases they carry in their heads, and build agentic tooling that compounds their productivity. The bar isn't "does it ship" - it's "do the researchers reach for it every day." Success is measured in researcher adoption and time saved, not features merged.
Agentic workflows are core to how we build. You should be fluent using them and confident designing systems where agents run in production - with clear judgment about where an agent earns its keep versus where deterministic code or a human-in-the-loop is the right call.
What You'll Do:
Lead development and delivery:
Own technical execution end-to-end: implementation, code review, and release.
Translate research workflows and feature requests into well-scoped tasks with realistic, risk-aware estimates the team can plan against.
Manage day-to-day execution: unblock people, sequence work, catch problems early.
Set and defend the technical bar: review rigor, testing discipline, documentation, architectural consistency.
Partner with the researchers - and amplify them:
Embed with malware researchers to understand their workflow and capture the tacit knowledge and edge cases no spec ever wrote down.
Translate that knowledge into reliable agentic tooling - and know when an agent is confidently wrong before it ever reaches a researcher.
Spend roughly 5-10% of your time doing actual malware research (with structured onboarding) to stay close to how the tool is used.
Be willing to tell a researcher when a proposed workflow won't automate well - and explain why.
Be the technical authority and mentor:
Make the hard architecture and design trade-off calls.
Mentor through code review, pairing, and design discussions. Raise the level of everyone around you.
Dive deep on the critical, difficult features and bug fixes yourself.
Design agentic workflows into the architecture from the start, and build the evaluations and guardrails that keep them trustworthy.

We are looking for an exceptionally strong software engineer to join our Technology organization. You will be part of a business-facing team responsible for building and evolving the core systems that power our Quant strategies. This is an AI-forward team: large language models (LLMs) and AI agents are part of our daily workflow - from writing and reviewing code, to automating operational toil, to building internal copilots and agentic tools that the whole team relies on. You won't just use AI to move faster; you'll help shape how it's applied across our engineering practice.
Design and build data pipelines and the data layer.
Develop and maintain the computational frameworks and infrastructure that run our quantitative strategies at scale.
Implement and enhance quant-related software systems for data, research, portfolio construction, and trading.
Work closely with Researchers, and Quants as a hands-on engineering partner, helping them turn ideas into robust production solutions.
Gain exposure to the full lifecycle of the systematic/quant business, from data ingestion through research, implementation.
Build and integrate AI-powered tooling - LLM agents, copilots, and automation - into engineering and research workflows.
Use AI coding assistants and agentic developer tools to ship higher-quality software faster.

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 evaluating AI agents that interpret complex biological data.

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 role)

What will you do?

Design, prototype, and build LLM-based agentic systems that reason over biological data, scientific literature, model outputs, and internal tools.
Develop agents capable of structured reasoning, hypothesis generation, explanation, planning, tool use, and iterative scientific analysis.
Build robust evaluation frameworks for agentic systems, including automated and human-in-the-loop evaluation pipelines.
Define and implement benchmarks, metrics, and test suites for measuring agent performance, including reasoning quality, biological grounding, factuality, robustness, reproducibility, and usefulness.
Work closely with AI researchers, computational biologists, immunologists, and product teams to translate scientific needs into measurable AI capabilities.
Create evaluation datasets and benchmark tasks that reflect real-world biological and therapeutic reasoning problems.
Analyze agent behavior, failure modes, hallucinations, tool-use errors, reasoning gaps, and grounding issues.
Contribute to the architecture of production-grade AI systems, including agent orchestration, retrieval, tool calling, memory, planning, and monitoring.
Stay up to date with the latest developments in LLMs, agentic AI, evaluation methodologies, and scientific AI systems.
Help turn research prototypes into reliable products used by internal teams and external partners.