Job Description About HiBob HiBob is a vibrant, fast-growing company on a mission to transform how modern, mid-sized companies manage their people. Our product serves over 5,300 customers globally, and our teams work in a true continuous delivery environment with modern microservices, automated QA, and frequent production deployments. About the Role We're looking for a hands-on Engineering Team Lead to join our Studio Team within the Platform Unit. This role combines people leadership, senior-level engineering across backend and frontend domains, and platform-oriented thinking, and plays a key part in building and scaling Bob's next-generation extensibility and AI-powered experience layer. As a Team Lead in the Studio team, you will lead a team of engineers while remaining actively involved in designing and implementing scalable systems that enable applications, widgets, workflows, and AI-driven experiences across the platform. If you're passionate about leading people, shaping technical direction, and staying hands-on while building zero-to-one platform capabilities and AI-native experiences - this role is for you. Why Join the Studio Team? Lead a team at the core of Bob's platform extensibility and future interaction model. Influence both user-facing experiences (applications, widgets, workflows) and the underlying platform and developer foundations. Work in a highly collaborative environment with strong Product, Platform, AI, and Design partners. Make a real impact at scale while growing as a technical and people leader. Job Requirements What You Bring Must Have - 7+ years of experience in software engineering with a strong backend or full-stack focus. - 3+ years of experience as an engineering team lead in a fast-paced environment. - Strong hands-on experience with modern backend technologies and system design. - Proven experience building and maintaining scalable distributed systems or platform capabilities. - Experience working with cloud platforms (AWS or equivalent). - Solid understanding of modern frontend technologies (e.g., React, component-based architecture). - Ability to balance technical depth, people leadership, and product/platform thinking. - Excellent communication skills in English (Hebrew is a plus). - Passion for both people management and hands-on engineering. Nice to Have - Experience building or operating AI / LLM-based systems in production. - Experience with extensibility platforms, APIs, or developer ecosystems. - Familiarity with workflow engines, orchestration systems, or event-driven architectures. - Background in developer experience (DevX), internal platforms, or shared services. - Experience with dynamic UI systems or design systems. - Experience mentoring engineers or leading internal guilds / tech forums. Job Responsibilities What You'll Do Technical Leadership & Hands-on Development - Lead the design, development, and operation of platform services and frameworks that power applications, widgets, workflows, and AI-driven experiences. - Take ownership of end-to-end delivery - from discovery and architecture through implementation, testing, deployment, and monitoring. - Address scalability, performance, and reliability challenges in systems enabling real-time, interactive, and AI-powered user experiences. Work closely with Platform, AI, Data, DevOps, Product, and Design teams to ensure seamless integration of Studio capabilities into the broader platform. Drive high engineering standards through code reviews, architecture discussions, testing practices, and automation. Team Leadership & Growth - Lead, mentor, and grow a team of engineers within the Studio team. - Own hiring, onboarding, coaching, and professional development of team members. - Foster a culture of ownership, collaboration, learning, and innovation. - Support engineers in growing their scope and taking end-to-end responsibility. Platform & Developer Experience - Contribute to and evolve Studio platform capabilities, including frameworks, SDKs, and tooling for building applications and workflows. - Improve developer and builder experience by enhancing extensibility, observability, CI/CD flows, and internal frameworks. - Enable and shape GenAI-powered experiences, including conversational interfaces, dynamic UI generation (GenUI), and data-driven workflows. - Collaborate with Architects and Platform leadership to align Studio with long-term platform and extensibility strategy. Benefits HiBob is a village filled with amazing people and we're especially proud of that. It's a place where Bobbers can be themselves. We're about fun, dreams, hopes and ambition, just as much as we are about precision, growth, and top performance. Becoming a Bobber means you'll receive competitive compensation, benefits, and pre-IPO equity alongside all of this: - Company share options plan - We have a flexible hybrid working model - Work from home allowance- to get your home office set up! - Payment for sick leave from the first day - 2 Social Impact days per year for volunteering - Annual Headspace subscription and wellness benefits - Awesome employee referral program- $2,500 for each successful referral with an additional ambassador programme - 10 bis or cibus/wolt- you get to pick! - Transportation allowance - Dog-friendly - Temporary remote work from anywhere in the world for up to 2 months (after 6 months of employment) - Fun company and team social events (locally and virtually with our global teams) - Bob balance days - 4 additional days within a calendar year - Enjoy a company-wide long weekend at the beginning of each quarter If this sounds like something you've been looking for, we'd love to have you. Come on, join our village!

Role Description We’re looking for a Senior Machine Learning Engineer to lead the development of experimental models, algorithms and prototype systems that push the boundaries of what’s possible with geospatial imagery analytics. Your work will bridge early-stage research and production—delivering high-quality, well-structured code that serves as a foundation for the next generation of Spexi’s geospatial intelligence products. - Design, train, and productionize models for aerial-image classification, object detection, and 2D/3D segmentation across diverse geographies, sensors, resolutions, and seasons. - Spearhead the integration and optimization of state-of-the-art foundation models for aerial segmentation, adapting SAM2-like capabilities and subsequent architectures through prompt engineering, fine-tuning, and distillation. - Engineer change detection and structure-change models capable of distinguishing real-world physical changes from acquisition noise and seasonal lighting variations. - Develop predictive models for trend forecasting, integrating time-series methods with spatial context to monitor vegetation growth, construction, and asset degradation. - Build Generative AI capabilities, including multimodal models and natural-language query systems that ground language in georeferenced pixels and semantic layers. - Design and operate scalable ML pipelines on AWS, leveraging SageMaker, S3, and Step Functions to move from research prototypes to production endpoints. - Track the frontier of research—including NeRFs, Gaussian Splatting, and diffusion models—translating relevant breakthroughs into shipped product capabilities. - Collaborate with photogrammetry and platform teams to ensure ML outputs maintain geospatial accuracy and align to coordinate reference systems. - Establish rigorous evaluation benchmarks and metrics to validate model performance under real-world production conditions. Qualifications - An M.S. and 5+ years of work experience in Computer Science, Computer Vision, Machine Learning, Remote Sensing, or a related quantitative field. - Proven experience in research-to-production translation: acting as the bridge between pure academia and commercial engineering. - At least 3 years of applied ML and computer vision experience transitioning models from research to production, ideally involving geospatial, aerial, or satellite imagery. - Deep, contemporary expertise in predictive AI for imagery, including classification, object detection, and segmentation. - Working knowledge of the SAM2 or similar algorithms—including fine-tuning, prompt design, and distillation. - Hands-on experience developing Generative AI capabilities, such as multimodal RAG, vision-language models, and diffusion-based pipelines. - Strong technical judgment regarding the selection of predictive vs. generative approaches. - Production experience operating ML pipelines on AWS, specifically utilizing SageMaker for training and hosting. - Experience managing large, complex imagery datasets in cloud environments. Requirements - A Ph.D. and 7+ years of work experience blending artificial intelligence with the physical sciences (e.g., Photogrammetry, Physics). - Deep expertise in geospatial and remote sensing workflows, including hands-on experience with georeferenced imagery. - Proven ability to adapt or pretrain geospatial foundation models to specialized remote-sensing tasks. - Specialized experience in 3D scene understanding, leveraging NeRFs, Gaussian Splatting, and point cloud segmentation. - Architectural experience designing multimodal RAG systems that integrate imagery, vector, and time-series data. - Background in fast-paced startup environments, with a demonstrated capability to translate experimental research into production-quality geospatial intelligence systems. Benefits - Remote-friendly environment (with a hub in Vancouver, Canada). - Flexible hours. - Medical, dental, and vision health benefits. - Spexi is an inclusive employer that values workplace equality, supports diversity, and respects the unique qualities each individual brings to the company.

Role Description The future is bright for the Porch Group, and we’d love for you to be a part of it as our Senior AIOps Engineer I. We are looking for a Senior AIOps Engineer I who will partner with product managers, platform engineers, data scientists, and machine learning engineers to ensure our AI and ML-powered systems are reliable, observable, secure, and cost-efficient in production. You will focus on how AI systems run in real-world environments: - Monitoring model performance and drift - Ensuring robust deployment pipelines - Managing incidents - Standing up new AI infrastructure - Improving the stability and scalability of our AI platform You'll help evolve our AI & ML Ops stack and operational processes so teams can ship AI features quickly and safely. Our AI/ML stack is based on Python and runs on Kubernetes (GKE) and Google Cloud Platform. We use tools such as: - Union Cloud (Flyte) for ML workflow orchestration - BentoML for model serving - Feast for feature stores - Label Studio for data annotation - BigQuery as our central data warehouse - Dataflow for streaming/batch data pipelines On the GenAI side, we operate a centralized LLM routing/gateway service across providers, batch prediction services for large-scale LLM inference, and are building out RAG infrastructure. You will maintain and harden this ecosystem — and stand up new infrastructure components as we expand our AI platform capabilities. Qualifications - Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred) - 4+ years of professional software engineering experience, including at least 3 years in commercial software engineering or production operations (SRE/DevOps/Platform/ML platform) - A curious and driven mindset, with a strong interest in how AI systems behave in production - Experience operating production systems at scale (preferably distributed, microservices- or Kubernetes-based architectures) - Strong GCP experience preferred - Hands-on experience with CI/CD tools and deployment automation - Understanding of basic machine learning concepts and model lifecycles - Ability to communicate effectively with data scientists, ML engineers, and product stakeholders - Strong computer science and programming fundamentals - Proven ability to write high-quality software using engineering best practices Requirements - Strong GCP experience, including hands-on work with: - GKE (Kubernetes Engine) - BigQuery (data warehousing, SQL, schema management) - Pub/Sub (event messaging) - Vertex AI (batch prediction, model deployment) - Cloud SQL, GCS, IAM - Experience with: - Infrastructure-as-code tools, preferably Terraform - Monitoring, logging, and alerting tools - Container management and Docker image lifecycle - Experience operating and administering self-hosted open-source tools on Kubernetes - Familiarity with LLM ecosystem tooling is a strong plus Benefits - Pay Range: INR 2,475,000 - 3,465,000 Annually - Comprehensive coverage for health, life, and financial well-being - Medical insurance, accident insurance, and retiral benefits - Wellness programs including: - 12 company-paid holidays - 2 flexible holidays - Privilege/earned leave - Casual/sick leave - Paid maternity and paternity leaves - Weekly wellness events Company Description Porch Group is a leading vertical software and insurance platform positioned to be the best partner to help homebuyers move, maintain, and fully protect their homes. We offer differentiated products and services, with homeowners insurance at the center of this relationship. - Providing the best services for homebuyers - Led by advantaged underwriting in insurance - To protect the whole home As a leader in the home services software-as-a-service (SaaS) space, we’ve built deep relationships with approximately 30 thousand companies that are key to the home-buying transaction. In 2020, Porch Group rang the Nasdaq bell and began trading under the ticker symbol PRCH. We are looking to build a truly great company and are JUST GETTING STARTED.

• Design, develop, and deploy LLM-powered and generative AI systems to enhance real estate operations, customer experience, and product innovation • Build and optimize agentic pipelines, including orchestration, tool integration, and feedback loops • Work closely with various teams (e.g., operations, marketing, product development) to identify and prioritize opportunities for AI and LLM integration • Architect and maintain scalable, production-grade AI systems using Python, cloud platforms, and modern AI frameworks (e.g., LangChain or similar orchestration tools) • Implement event-driven and asynchronous architectures to support high-performance AI applications • Apply prompt engineering and LLM fine-tuning approaches where applicable to improve system performance • Stay informed about the latest trends and advancements in Generative AI and LLM ecosystems to drive innovation and maintain competitive advantage • Troubleshoot, optimize, and refine AI systems to ensure high performance, reliability, and scalability in production environments • Maintain thorough documentation of systems, processes, and results; communicate findings and progress to stakeholders