דרושים » הנדסה » Staff engineer - AI Builder

This is a Principal AI Software Engineer role at the heart of our companys AI Engineering group-where algorithms, infrastructure, and products intersect. Youll lead the evolution and scaling of our companys AI architecture, ensuring reliability, efficiency, and impact across global clinical workflows. The role combines technical depth with cross-functional influence, strengthening how our AI systems are built, integrated, and delivered to drive ongoing impact for patients and care teams.
Responsibilities
Lead cross-domain engineering initiatives connecting AI algorithms (CV, NLP, and deep-learning systems), software, and data systems-from design to delivery.
Stay hands-on, contributing to AI-serving (training and inference), integration, and orchestration components while setting high technical standards.
Partner with Algo teams to connect algorithmic innovation with strong system design, ensuring smooth delivery across Product and Engineering.
Guide architectural evolution, ensuring efficiency, consistency, and maintainability across AI workflows.
Communicate direction and priorities clearly, aligning teams around technical goals.
Mentor engineers and promote technical excellence, sound design, and shared best practices.
Foster collaboration and shared ownership, helping maintain a culture that values clarity, curiosity, and technical excellence.
Uphold quality and reliability-including clean code, testing, and CI/CD discipline-while evaluating and adopting new tools that enhance performance and long-term scalability.

What You'll Actually Be Doing
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code (human or AI-generated) for correctness, quality, security, and performance.
Build new and exciting components in an ever-growing and evolving market technology to provide scale and efficiency.
Develop high-quality, production-ready code that can be used by millions of users of our cloud platform.
Make design decisions on the basis of performance, scalability, and future expansion.
Work in a Hybrid Engineering model and contribute to all phases of SDLC including design, implementation, code reviews, automation, and testing of the features.
Build efficient components/algorithms on a microservice multi-tenant SaaS cloud environment.
Code review, mentoring junior engineers, and providing technical guidance to the team (depending on the seniority level).

As a Staff Software Engineer, you will serve as a technical authority and play a pivotal role in scaling our core platform. Your focus will span architecture, security research, and engineering excellence:

Lead & Architect: Design, build, and scale high-performance software while driving architectural best practices across the R&D organization.
Security & AI Innovation: Drive deep-dive research into cloud, SaaS ecosystems, and AI agents to build proactive detection models, mitigation strategies, and threat intelligence.
Engineering Excellence: Elevate code quality, testing standards, system monitoring, and documentation across teams.
Cross-Functional Ownership: Partner closely with Product and R&D to translate complex security requirements into scalable technical solutions.
Scale & Optimize: Identify and resolve systemic performance bottlenecks and architectural challenges.
Mentorship & Leadership: Act as a technical mentor, driving engineering standards and fostering a strong knowledge-sharing culture.

In this role, you will architect and implement large-scale distributed systems, low-level platform components, and high-performance services that underpin core product stack. You will drive technical strategy across system reliability, performance, and scalability, partnering closely with product, infrastructure, and data engineering teams to deliver systems that operate at global scale with high availability and efficiency.
Software Engineer, Systems Responsibilities
Architect and implement large-scale distributed systems and platform services that support high-throughput, low-latency workloads across Meta's product infrastructure
Lead the technical design of systems components including storage layers, compute pipelines, networking abstractions, and service orchestration frameworks
Identify and resolve systemic performance bottlenecks through instrumentation, profiling, and targeted optimization across the full systems stack
Define and enforce service level objectives for owned systems, building dashboards, alerting pipelines, and runbooks to reduce mean time to mitigation during incidents
Drive reliability improvements by reducing failure surface, designing resilient rollout strategies, and leading regular resiliency and overload testing exercises
Collaborate with cross-functional partners across product engineering, infrastructure, and data science to align system architecture with evolving product and business requirements
Establish and evolve coding standards, architectural patterns, and engineering best practices for systems development across the broader organization
Leverage AI-assisted development workflows to accelerate design iteration, code generation, and systems analysis, applying sound judgment on when to rely on AI versus deep systems expertise
Mentor other engineers on systems design principles, debugging methodologies, and production operations, and contribute to onboarding programs for new team members
Lead incident retrospectives, identify root causes of complex production failures, and drive implementation of systemic improvements to prevent recurrence

We are looking for an exceptional, driven, and technically visionary Staff Full-Stack Engineer to help shape the future of our platform and accelerate our mission to simplify taxes for everyone. Youll play a central role in designing and building scalable systems and tools that enable our teams to deliver exceptional user experiences with speed and confidence. If you're passionate about technical leadership, enjoy solving complex architectural challenges, and want to make a meaningful impact on millions of users, wed love to have you join us.

Responsibilities

Critical, cross-functional engineering initiatives from design to development and deployment, ensuring quality and scalability.

Participate in technical decision-making and knowledge sharing across multiple teams.

Collaborate with Product, UX, and other stakeholders to deliver seamless and data-driven experiences.

Evaluate and introduce new tools, technologies, and practices that raise the bar for engineering excellence.

Guide by example - service ownership, observability, and operational best practices across the engineering organization.

Promote a culture of high standards, clean code, testing, and continuous improvement.

We are looking for a Senior Backend Engineer to join a high-impact, multi-disciplinary team building a customer-facing product trusted by health industry specialists. This team is solving complex technical challenges with a modern stack to power core growth engine.
In this role you will collaborate closely with other R&D, Product, and Data teams.
What Youll Do :
Architect & Build: Design and construct high-performance, production-grade backend systems and applicative services built for reliability and maintainability.
Own E2E Lifecycle: Take complete end-to-end ownership of backend features from concept to production- including architectural design, testing, shipping, and active monitoring.
Cross-Domain Collaboration: Partner closely with Product Managers, Data Scientists, and R&D teams to translate complex data pipelines and product needs into scalable technical solutions.
Elevate Engineering Standards: Participate in rigorous code reviews, establish architectural best practices, and help mentor team members across the organization.

We're looking for an experienced and passionate ML Engineering Team Lead to lead our ML Engineering team and shape the next generation of our AI infrastructure. This is a hands-on leadership role where you'll combine technical leadership, software architecture, and people management to build scalable, production-ready AI systems running on edge devices.
About The Role:
Lead, mentor, recruit, and grow a team of software engineers, fostering a culture of ownership, collaboration, and continuous improvement.
Own the team's technical roadmap, architecture, execution, and project prioritization, aligning delivery with business goals.
Design, build, and maintain scalable software and ML infrastructure across cloud and edge environments.
Partner with AI Researchers to productionize Computer Vision and Deep Learning models into reliable, high-performance systems.
Design and optimize inference pipelines with a focus on scalability, latency, and reliability.
Drive engineering excellence through architecture reviews, code reviews, development best practices, and modern AI-assisted engineering workflows.

As the Development Team Lead for HiredScore Sourcing Products, you will manage and mentor a cross-functional team of Full-Stack Engineers, Backend Engineers, and QA Automation Specialists.
Your team's core mission will be to translate AI/ML insights into seamless, scalable features that drive efficiency in recruiter workflows-specifically through resurfacing past applicants, prospects, and internal talent. You will own the entire development lifecycle for your products, from core API implementation to deploying user-facing applications and integration layers (web UIs, RPA/workflows, MS Teams bots)
Key Responsibilities:
Lead, mentor, and grow the diverse engineering team, ensuring effective collaboration between backend, full-stack, and QA functions. Drive best practices in development, code quality, and delivery speed.
Design, document, and oversee the implementation of scalable, reliable, and secure systems, ensuring seamless integration between backend AI services and the user-facing product layers.
Ensure the team maintains a strong quality and automation culture by providing strategic direction to QA specialists and upholding high standards for testing and product stability.
Drive development across the full spectrum of product surfaces: APIs (GraphQL/REST), Web Applications (React/Angular/Vue), and specialized engagement layers.
Ensure efficient consumption of AI outputs by downstream systems, particularly through robust queue-based architectures (like RabbitMQ) and workflow automation (RPA).
Partner closely with Product Management, Data Science, and other engineering teams to translate business requirements into technical execution.

We are looking for a proactive and skilled Software Infrastructure Developer with a strong Agentic AI focus to join our team. In this role, you will design and build the infrastructure, tooling, and AI-powered automation that keep our engineering systems reliable, scalable, and intelligent.

You will be responsible for embedding GenAI and agentic capabilities into our internal developer platforms and CI/CD workflows - creating frameworks, libraries, and multi-agent systems that accelerate automation, improve system stability, and unlock new levels of engineering productivity. This is a hands-on role for someone who thrives at the intersection of infrastructure engineering and applied AI.

The ideal candidate brings deep technical expertise in Python (FastAPI) and/or TypeScript, Kubernetes, CI/CD (Jenkins, GitHub Actions), containerization, and cloud infrastructure - combined with hands-on experience building LLM-powered applications, agents, MCP servers, and RAG pipelines.

What you'll be doing:
AI Platform Development: Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications.
Agentic Workflow Development: Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval.
AI-Powered Infrastructure & CI/CD: Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows.
AI Observability & Evaluation: Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis.
AI Security & Governance: Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably.
End-to-End Platform Ownership: Own AI platform capabilities from architecture through production and build reusable solutions that accelerate adoption across engineering teams.
Cross-Functional Collaboration: Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions.
AI Research & Innovation: Evaluate emerging AI frameworks, models, and cloud technologies and promote their adoption where they provide measurable value.

Were looking for a senior engineer who is ready to move beyond traditional software development and take full ownership of product delivery.

This is not a classic backend engineering role. You will work directly with Product to turn ideas and requirements into production-ready solutions, make the technical decisions, and use AI coding agents as a core part of your engineering workflow.

You will own the outcome, not just the code. AI-generated code is your code, and you are accountable for its quality, security, performance, and maintainability.


Own product features end-to-end, from requirements through production and monitoring.
Work with Product to translate business problems into clear technical solutions and execution plans.
Design scalable and maintainable solutions, including architecture, APIs, data models, and integrations.
Use AI coding agents to accelerate implementation, testing, debugging, and automation.
Review and improve AI-generated code, ensuring high standards for correctness, security, performance, and maintainability.
Make independent technical decisions and communicate architectural trade-offs clearly.
Contribute across backend, frontend, QA, CI/CD, and infrastructure when needed to deliver a feature.
Investigate and solve complex production and performance issues.
Continuously improve how we build, test, and ship software using AI-native development practices.