Senior AI Engineer — Exploration & Prototyping

📍 Job Overview

Job Title: Senior AI Engineer — Exploration & Prototyping

Company: Kaltura

Location: Bnei Brak, Tel Aviv District, Israel

Job Type: Full-time

Category: AI/Machine Learning Engineering, Research & Development

Date Posted: 2026-09-10

Experience Level: 7+ years

Remote Status: Hybrid

🚀 Role Summary

• Spearhead technical exploration and prototyping initiatives to address critical platform challenges and drive innovation in AI and video technologies.

• Conduct in-depth technical evaluations of emerging frameworks, protocols, and AI models, providing data-driven recommendations to influence architectural decisions.

• Develop and execute prototypes to de-risk new technologies and validate hypotheses, ensuring the selection of robust and production-ready solutions.

• Collaborate closely with platform, research, and forward-deployed teams to integrate findings and ensure seamless knowledge transfer for production implementation.

• Act as a key individual contributor, driving research initiatives independently and delivering defensible conclusions that shape the future of Kaltura's AI capabilities.

📝 Enhancement Note: This role is positioned as a critical R&D function within Kaltura, focusing on strategic technical exploration rather than direct product feature development. The emphasis on "closing open decisions" and "owning build-versus-adopt recommendations" highlights a proactive, evidence-based approach to technology selection and innovation in the rapidly evolving AI landscape.

📈 Primary Responsibilities

• Run technical spikes to investigate and evaluate critical platform decisions, including agent orchestration frameworks, real-time transport protocols, memory management strategies, agent interoperability standards, LLM selection and routing, evaluation harnesses, and production library/stack choices.

• Build and deploy prototypes rapidly to de-risk technological choices, proving or disproving concepts without becoming overly attached to the code, facilitating swift iteration and decision-making.

• Conduct deep-dive code analysis of candidate frameworks and libraries, going beyond documentation to understand true capabilities and limitations for production readiness.

• Design and implement measurement harnesses to objectively assess performance metrics such as latency, cost, and failure behavior of potential solutions.

• Formulate and present build-versus-adopt recommendations for platform infrastructure, frameworks, and libraries, including a thorough analysis of the risks and costs associated with incorrect decisions.

• Author concise, decisive technical documents summarizing spike findings, evidence, rejected options, and clear reasoning to enable actionable insights for engineering teams.

• Facilitate clean handoffs of learned knowledge and successful prototypes to production teams, remaining available for support during their integration phase.

• Continuously monitor and research advancements in agentic infrastructure, real-time frameworks, and adjacent AI tooling, proactively identifying and introducing technologies that enhance Kaltura's capabilities.

📝 Enhancement Note: The responsibilities are heavily focused on deep technical investigation and evidence-based decision-making. The role requires a blend of research acumen, hands-on development for rapid validation, and strong technical communication skills to influence architectural direction. The emphasis on "not shipping to customers" and "not owning a subsystem" clarifies the exploratory nature of the role, differentiating it from a typical product engineering position.

🎓 Skills & Qualifications

Education:

• Required: B.Sc. in Computer Science, or an equivalent technical field.

• Strongly Preferred: M.Sc. in Computer Science, Machine Learning, or a related field.

Experience:

• Required: 7+ years of industry experience in software engineering, ML engineering, or research engineering roles, with demonstrable real ownership of production systems.

• Strongly Preferred: Early-employee or founding-engineer experience, where evaluating the entire technology stack was a core responsibility.

Required Skills:

• Genuine technical breadth: Experience across backend services, runtime, and infrastructure; comfortable working near ML systems without direct model ownership; ability to manage multiple unfamiliar technical domains concurrently.

• Strong Python skills: Proven ability to rapidly develop functional prototypes and solutions.

• Track record of technical evaluations: Demonstrated experience in comparing real-world options, producing objective evidence, and influencing organizational decisions based on findings.

• Evidence-based approach: Experience designing and implementing benchmarks or measurement harnesses, with examples of data contradicting initial intuition.

• Real-time, streaming, or latency-sensitive systems: Hands-on experience with systems requiring high throughput and low latency.

• LLMs and agentic systems: Practical experience with Large Language Models, including orchestration, tool calling, and understanding their production behavior and failure modes.

• Individual Contributor (IC) proficiency: Self-directed and able to complete exploration projects independently, with a focus on landing tangible outcomes.

• SaaS and Cloud Environments: Experience in fast-moving SaaS companies and proficiency with cloud platforms (AWS, GCP, or Azure).

Preferred Skills:

• Experience with voice or real-time agent frameworks (e.g., Pipecat, LiveKit Agents).

• Expertise in WebRTC, media transport, or streaming infrastructure.

• Knowledge of agent memory systems, MCP, or agent interoperability standards.

• Ability to critically evaluate research literature and translate findings into practical recommendations.

• Contributions to open-source projects, public technical writing, or published evaluations.

📝 Enhancement Note: The emphasis on "genuine technical breadth" and "evidence over intuition" suggests a need for a versatile engineer who can bridge the gap between theoretical concepts and practical implementation. The requirement for "real ownership of production systems" indicates that while this role is exploratory, it's grounded in practical, operational realities.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Technical Spike Documentation: Examples of concise, data-driven documents detailing investigations into specific technical questions, including methodology, findings, and clear recommendations.

• Prototype Demos: Evidence of rapid prototyping skills, showcasing functional prototypes built to validate technical hypotheses or de-risk new technologies.

• Measurement & Benchmarking Frameworks: Demonstrations of designed and implemented benchmarks or measurement harnesses used to evaluate system performance (e.g., latency, throughput, cost, failure rates).

• Build-vs-Adopt Analysis: Case studies or examples of analyses performed to compare in-house development versus adopting third-party solutions, including risk assessment and cost-benefit evaluations.

Process Documentation:

• Exploration Methodology: Clear articulation of a structured approach to technical exploration, from problem definition and research to experimentation and documentation.

• Knowledge Transfer Artifacts: Examples of how complex technical findings and learnings were effectively communicated and transferred to development teams for production implementation.

• Performance Analysis Reports: Samples of reports detailing the performance characteristics of evaluated technologies, including metrics, analysis, and actionable insights.

📝 Enhancement Note: Given the role's focus on exploration and recommendation, a portfolio demonstrating the ability to systematically investigate, prototype, measure, and document technical findings is crucial. The emphasis is on the process of exploration and the quality of evidence provided, rather than just the code itself.

💵 Compensation & Benefits

Salary Range:

• Based on industry benchmarks for Senior AI Engineers with 7+ years of experience in Israel, particularly in the Tel Aviv tech hub, the estimated salary range is ₪35,000 - ₪55,000 per month gross. This range accounts for the specialized nature of AI/ML roles, the required experience level, and the cost of living in the region. Benefits:

• Hybrid, flexible work environment: Offers adaptability for work-life balance.

• Extended private health insurance: Comprehensive coverage, including mental health support.

• Personal and professional development programs: Opportunities for continuous learning and skill enhancement.

• Occasional Cross company long weekends: Additional time off to promote well-being and prevent burnout.

Working Hours:

• Standard full-time hours (estimated 40 hours/week), with flexibility inherent in the hybrid work model. Emphasis is placed on results and deliverables rather than strict adherence to office presence.

📝 Enhancement Note: The salary range is an estimate based on general market data for senior engineering roles in Israel. Actual compensation will depend on the candidate's specific qualifications, interview performance, and Kaltura's internal compensation structure. The benefits package highlights a focus on employee well-being and professional growth, common in competitive tech environments.

🎯 Team & Company Context

🏢 Company Culture

Industry: Video technology, SaaS, AI/ML. Kaltura operates in the rapidly evolving digital video space, enabling organizations to manage and deliver video experiences. The integration of AI is a strategic imperative for enhancing these experiences and operational efficiency.

Company Size: 700+ employees globally. This indicates a mature, established company with established processes, but still agile enough to foster innovation and individual initiative.

Founded: 2006. With over 15 years in the market, Kaltura has significant experience and a strong market presence, now leveraging cutting-edge technologies like AI.

Team Structure:

• The Senior AI Engineer will likely be part of a dedicated R&D or Advanced Technology group, working alongside other specialized engineers and researchers.

• This role sits alongside platform, research, and forward-deployed teams, implying a collaborative but distinct function focused on exploration.

• The structure encourages borrowing context freely from various teams, suggesting a matrixed or project-based collaboration model rather than rigid silos. Methodology:

• Data-Driven Decision Making: The role emphasizes running spikes, building prototypes, and collecting measurable data (latency, cost, failure) to inform recommendations.

• Iterative Exploration: The process involves rapid prototyping and evaluation, with an emphasis on learning and moving on without deep attachment to code.

• Evidence-Based Recommendations: Final outputs are decisive documents backed by solid evidence, designed for immediate action by other engineering teams.

Company Website: https://corp.kaltura.com/

📝 Enhancement Note: Kaltura's description highlights a culture that values initiative, collaboration, and growth, with a hybrid work model. The emphasis on "everyone gets a say" and "initiative is always encouraged" suggests an environment where individual contributions are recognized and valued, particularly in a specialized role like this.

📈 Career & Growth Analysis

Operations Career Level: This role represents a senior individual contributor (IC) path within the AI/ML engineering domain. It's a specialized track focused on deep technical investigation and strategic technology assessment, rather than people management. The influence comes from the quality of research and the defensibility of recommendations.

Reporting Structure: While not explicitly stated, the role sits "alongside the platform, research, and forward-deployed teams." This suggests reporting to a lead within an advanced technology, R&D, or AI research group, with direct interaction and collaboration with multiple engineering teams.

Operations Impact: The impact is indirect but significant. By providing evidence-based recommendations on crucial technologies (protocols, LLMs, frameworks), this role directly influences the future architecture and technological direction of Kaltura's platform. This can lead to improved performance, reduced costs, enhanced capabilities, and faster innovation cycles across the entire product suite.

Growth Opportunities:

• Technical Specialization: Deepen expertise in cutting-edge AI technologies, agentic systems, real-time protocols, and cloud infrastructure.

• Architectural Influence: Become a trusted advisor on technology selection and architectural decisions, shaping the technical roadmap.

• Cross-Functional Leadership: Lead technical investigations and present findings to diverse engineering teams, developing leadership through technical expertise.

• Industry Insight: Stay at the forefront of AI research and development, potentially contributing to public discourse through writing or open-source work.

• Mentorship: Potentially mentor junior engineers on best practices for technical evaluation, prototyping, and research.

📝 Enhancement Note: This role offers a unique growth path for engineers who excel at deep technical analysis and strategic thinking, providing a high level of autonomy and influence without the traditional management track. The focus is on becoming a subject matter expert and a key decision-driver for technology adoption.

🌐 Work Environment

Office Type: Kaltura offers a hybrid work model, suggesting a combination of remote work and in-office presence. The office environment in Bnei Brak is likely designed to foster collaboration and innovation.

Office Location(s): Bnei Brak, Tel Aviv District, Israel. This is a major technology hub in Israel, offering access to a vibrant tech community and talent pool.

Workspace Context:

• Collaborative Environment: The hybrid model and emphasis on cross-functional collaboration mean opportunities to work with diverse teams, share knowledge, and engage in problem-solving sessions.

• Tools & Technology: Access to necessary cloud environments (AWS, GCP, Azure), development tools, and potentially specialized AI/ML hardware or platforms for prototyping and experimentation.

• Team Interaction: Regular interaction with platform engineers, researchers, and forward-deployed teams to gather context, share findings, and ensure seamless integration of recommendations.

Work Schedule: While standard full-time hours are expected, the hybrid and flexible nature of the work environment allows for adaptability. The focus is on achieving project milestones and delivering high-quality research outputs, allowing for some flexibility in daily scheduling.

📝 Enhancement Note: The hybrid model indicates a balance between focused individual work (potentially remote) and collaborative sessions or team syncs (in-office). This environment is well-suited for roles requiring deep concentration alongside active engagement with various engineering stakeholders.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will likely review your resume and conduct an initial call to assess basic qualifications, experience, and cultural fit.

• Technical Screening/Challenge: Expect a coding challenge or a deep-dive technical discussion focused on Python, system design, AI/ML concepts, and your approach to problem-solving and evaluation. This may involve a take-home assignment or a live coding session.

• Portfolio Review: A key stage will involve presenting your portfolio. Be prepared to walk through specific examples of technical spikes, prototypes, benchmark results, and recommendation documents. Focus on the process you followed and the impact of your findings.

• Team/Hiring Manager Interviews: These interviews will delve deeper into your experience with production systems, your ability to work independently, your communication skills, and how you handle ambiguity and make defensible decisions. Expect scenario-based questions.

• Final Round: This may involve a discussion with senior leadership or a broader technical team to ensure alignment and confirm suitability for the role's strategic impact.

Portfolio Review Tips:

• Structure is Key: Organize your portfolio logically, perhaps by project type (spike, prototype, evaluation) or by technical domain.

• Show, Don't Just Tell: For each entry, clearly state the problem, your approach, the tools/methods used, the results (with data!), and the final recommendation/outcome.

• Quantify Impact: Whenever possible, use numbers to demonstrate the value of your work (e.g., "reduced latency by X%", "identified cost savings of Y", "de-risked a Z million dollar investment").

• Highlight Process: Emphasize your methodology for investigation, measurement, and decision-making. This role values how you arrive at conclusions as much as the conclusions themselves.

• Be Concise: Focus on the most impactful examples. For a Senior role, quality and depth over quantity are preferred.

• Tailor to the Role: Highlight experiences most relevant to LLMs, agentic systems, real-time protocols, and evaluating infrastructure/frameworks.

Challenge Preparation:

• Master Python: Be ready for complex coding challenges, focusing on efficiency, data structures, and algorithms.

• System Design: Practice designing scalable, reliable, and performant systems, particularly those involving real-time data or complex AI pipelines.

• AI/ML Fundamentals: Refresh your understanding of core ML concepts, LLM architectures, agentic patterns, and common evaluation metrics.

• Scenario-Based Questions: Prepare to discuss how you would approach specific technical challenges, handle conflicting data, make trade-offs, and communicate complex ideas.

• Research & Empathy: Research Kaltura's products and recent AI initiatives. Understand their potential challenges and how your role can contribute.

📝 Enhancement Note: The interview process heavily emphasizes practical application and demonstrable skills in technical evaluation and recommendation. A well-curated portfolio that showcases the candidate's ability to systematically investigate, validate, and articulate technical findings will be critical for success.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary), potentially others for specific prototyping needs.

• Cloud Platforms: AWS, GCP, or Azure (proficient usage required for deployment and testing).

• AI/ML Frameworks: Experience with common ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and a deep understanding of LLM APIs and frameworks (e.g., LangChain, LlamaIndex, OpenAI API, Hugging Face Transformers).

• Containerization & Orchestration: Docker, Kubernetes (likely for deploying prototypes and testing environments).

Analytics & Reporting:

• Benchmarking Tools: Familiarity with tools and techniques for performance measurement, load testing, and latency analysis.

• Data Analysis Libraries: Pandas, NumPy for processing and analyzing experimental data.

• Visualization Tools: Matplotlib, Seaborn, or BI tools for presenting findings clearly.

CRM & Automation:

• (Less directly relevant to this role, but understanding how systems integrate is beneficial). Familiarity with CI/CD pipelines and MLOps principles for managing prototype deployments and experiments.

📝 Enhancement Note: Proficiency in Python and cloud environments is a baseline. Deep, hands-on experience with LLMs, agentic systems, and the ability to build and measure prototypes are central. Familiarity with real-time data processing and streaming technologies is also highly relevant.

👥 Team Culture & Values

Operations Values:

• Innovation & Exploration: A strong drive to discover and evaluate new technologies that can push the boundaries of what's possible.

• Data-Driven Decision Making: Commitment to using objective evidence and measurable results to guide technical choices.

• Technical Excellence: A high standard for code quality, system design, and rigorous evaluation.

• Collaboration & Knowledge Sharing: Valuing teamwork and the open exchange of ideas and learnings across different engineering disciplines.

• Impact & Ownership: A focus on delivering tangible outcomes and recommendations that have a significant impact on the platform's direction.

Collaboration Style:

• Proactive Engagement: Actively seeking out context and engaging with platform, research, and forward-deployed teams to understand their needs and challenges.

• Clear Communication: Articulating complex technical concepts and findings effectively through documentation and presentations.

• Evidence-Based Debate: Participating in technical discussions with data and logical reasoning to arrive at the best solutions.

• Cross-Functional Support: Willingness to transfer knowledge and support other teams in adopting new technologies based on exploration findings.

📝 Enhancement Note: Kaltura's stated culture emphasizes a blend of individual initiative and collaborative spirit. For this role, the ability to operate autonomously while effectively integrating with and influencing multiple teams will be key to success.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving Landscape: The AI and video technology fields are constantly changing, requiring continuous learning and adaptation to stay ahead.

• Balancing Exploration and Production Reality: Ensuring that exploratory work is grounded in practical considerations relevant to production environments (scalability, cost, reliability).

• Making "No" Decisions: The ability to confidently recommend against adopting a technology, even if promising, based on solid data, can be challenging but is crucial for the role.

• Technical Ambiguity: Tackling open-ended questions with no pre-defined solutions requires strong problem-solving skills and creativity.

Learning & Development Opportunities:

• Deep Dive into AI/ML & Video Tech: Extensive exposure to cutting-edge research and development in LLMs, agentic systems, real-time streaming, and cloud-native architectures.

• Architectural Influence: Opportunity to shape the technical direction of a leading video platform.

• Industry Exposure: Potential to attend conferences, engage with the research community, and contribute to public knowledge through writing or open-source.

• Mentorship & Skill Enhancement: Access to professional development programs and the opportunity to learn from experienced engineers and researchers within Kaltura.

📝 Enhancement Note: This role is ideal for someone who thrives on tackling complex, open-ended technical problems and enjoys the process of discovery. The challenges are inherent to working at the forefront of technology, and the growth opportunities are substantial for those who excel in this environment.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to evaluate a complex new technology for production. What was your process, what data did you collect, and what was the outcome?" (Focus on your methodology, data, and defensible recommendation).

• "How would you approach evaluating different LLM orchestration frameworks for a real-time agent system? What metrics would you prioritize, and how would you measure them?" (Demonstrate understanding of LLMs, agents, real-time constraints, and benchmarking).

• "Imagine you're tasked with determining whether to build a custom real-time transport protocol or adopt an existing open-source solution. How would you scope this investigation and what factors would you consider?" (Showcase your ability to frame technical trade-offs and risk assessment). Company & Culture Questions:

• "What interests you most about Kaltura and this specific role in AI exploration?" (Connect your passion for AI/ML, exploration, and video technology to Kaltura's mission).

• "How do you stay current with the rapidly evolving AI and ML landscape?" (Highlight your continuous learning habits and sources of information).

• "Describe your experience working as an individual contributor in a fast-paced environment. How do you manage your time and priorities?" (Emphasize self-direction, focus, and results-orientation). Portfolio Presentation Strategy:

• Start with Impact: Begin with your most compelling project that best showcases your ability to drive technical decisions.

• Narrative Flow: Tell a story for each project: the problem, your unique approach, the challenges, the data-driven solution, and the concrete recommendation or outcome.

• Visual Aids: Use clear diagrams, charts, and code snippets (if appropriate) to illustrate your points.

• Quantify Everything: Present metrics clearly and explain their significance.

• Be Ready for Deep Dives: Anticipate questions about your methodology, alternative approaches, and the limitations of your work. Be prepared to defend your conclusions with evidence.

📝 Enhancement Note: The interview process will heavily scrutinize your ability to perform rigorous technical analysis, make data-backed recommendations, and communicate complex findings effectively. A strong portfolio presentation is paramount.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided link on Comeet.

• Tailor your Resume: Highlight experience with Python, LLMs, agentic systems, cloud platforms (AWS/GCP/Azure), real-time/latency-sensitive systems, technical evaluation, benchmarking, and prototyping. Quantify achievements wherever possible.

• Prepare Your Portfolio: Curate 2-3 of your most impactful projects that demonstrate your ability to conduct technical spikes, build prototypes, evaluate technologies, and provide data-driven recommendations. Focus on the process and quantifiable outcomes.

• Practice Presentation Skills: Rehearse presenting your portfolio and answering technical/scenario-based questions. Be ready to articulate your thought process clearly and concisely.

• Research Kaltura: Understand their core business (video technology), their mission, and their current focus on AI. This will help you tailor your answers and demonstrate genuine interest.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

---