You are a Systems Engineer at heart, but you are ready to apply LLMs, RAG, and predictive modeling to solve infrastructure challenges at scale.

Our Team's Stack:

• Languages: Python, Go, bash/sh
• Observability: Prometheus, Victoria Metrics, Grafana
• OS & Kernel: Linux Kernel (custom build), Ubuntu
• Hardware: Intel/AMD/ARM CPUs, Nvidia GPUs, DPUs, Infiniband and Ethernet NICs
• Containerization: Docker, Kubernetes (k8s), KubeVirt, containerd, kubelet

Responsibilities:

• Pipeline Architecture: Design, maintain, and automate reproducible OS image build pipelines for our massive fleet of GPU-accelerated servers.
• Kernel Distribution: Collaborate with kernel engineers to package, validate, and distribute custom Linux builds across Intel, AMD, and ARM architectures.
• Dependency Management: Build tooling to manage dependencies, versioning, and release workflows, ensuring hermetic builds.
• Telemetry & Metrics: Standardize the collection of build metrics to create a baseline for future AI modeling.
• 'Smart' CI/CD & Auto-Remediation: Architect AI agents that ingest and analyze build logs in real-time. Develop systems that auto-triage errors, categorize failure patterns, and generate context-aware fix suggestions for engineering teams.
• Predictive Regression Modeling: Design ML workflows that utilize historical performance data to detect kernel and OS regressions (latency, throughput, stability) in staging environments before they impact production.
• Dynamic Kernel Tuning: Implement closed-loop feedback systems that analyze real-time system metrics and automatically suggest or apply sysctl parameter optimizations for specific customer workloads.
• Next-Gen ChatOps: Engineer LLM-driven interfaces for Slack/internal tools, enabling stakeholders to query build statuses, request log summaries, or provision resources using natural language commands.

Requirements:

• 4+ years of professional experience in Linux Systems Engineering, Release Engineering, or DevOps.
• Deep knowledge of Linux internals (boot process, kernel modules, networking stack).
• Experience with package management (Debian/Ubuntu) and build systems.
• Strong proficiency in Python (essential for the AI integration aspects of this role).
• Demonstrable experience integrating API-based AI models (OpenAI, Anthropic, or local open-source models) into software workflows.
• Understanding of RAG (Retrieval-Augmented Generation) architectures for querying technical documentation or logs.
• Experience building event-driven automation (e.g., using webhooks to trigger analysis agents).
• Familiarity with data structures required for vector search or time-series analysis.

Nice-to-haves:

• Experience with Kubeflow or MLFlow.
• Background in High-Performance Computing (HPC).
• Experience fine-tuning small language models (SLMs) for code or log analysis tasks.

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In this role, you will serve as a guardian of our codebase through implementation, rigorous peer reviews, automated testing, and comprehensive deployment support, ensuring the architectural integrity of our environments.

Key Responsibilities:

Technical Implementation: Lead the translation of user stories into robust, scalable technical designs. Responsible for the end-to-end execution of features, ensuring alignment with organisational standards.

Solution Design and Architecture: Architect and design secure, scalable solutions by translating complex business requirements into technical specifications and solution designs that leverage the Salesforce platform, particularly within the Sales and CPQ domains.

Quality Assurance and Testing: Ensure 100% unit test coverage for all new logic. Build and maintain an automated test regression suite to minimise manual overhead and proactively identify regressions.

Code and Peer Mentorship: Conduct thorough Pull Request (PR) reviews, providing constructive feedback to junior and mid-level developers to maintain code quality and consistency.

Deployment and Release Support: Manage the deployment cycle, including documenting all changes, securing necessary Jira approvals, resolving environment-specific test issues, and performing final validation in Production.

Global Collaboration & Ownership: Demonstrated strong ownership and the ability to communicate effectively and collaborate seamlessly with cross-functional, global teams and stakeholders.

Required Qualifications:

Salesforce Development Expertise (5+ years): Proven proficiency in Salesforce development, including Apex, Process/Flow Automations, and Lightning Web Components (LWC).

Software Development Acumen (3+ years): Strong background in general software engineering principles, with significant experience in Java, Python, or JavaScript.

Domain Specific Knowledge: In-depth knowledge and practical experience within Sales Cloud and Configure, Price, Quote (CPQ) domains are essential.

Advanced AI Agent Frameworks & Data Processing: Expertise in modern AI agent or agentforce frameworks, orchestration techniques, Retrieval-Augmented Generation (RAG) pipelines, and advanced vector search methodologies.

CI/CD Pipeline Proficiency: Hands-on experience with continuous integration and continuous deployment (CI/CD) pipelines, specifically utilising Jenkins and Salesforce DX (SFDX) for efficient development and release.

Secure Salesforce Solution Development: Deep understanding and practical application of Salesforce security features, adherence to data privacy regulations, and implementation of best practices for secure solution development.

Communication, & Problem-Solving: Possesses excellent communication and robust problem-solving abilities. Capable of independently driving technical direction, mentoring team members, fostering collaboration, and effectively managing all project aspects.

Preferred Qualifications (Extra Credit):

Salesforce certifications, such as Platform Developer I/II, CPQ Administrator, Application Architect, or Technical Architect.

Experience designing or implementing advanced Salesforce DevOps practices.

Previous work in a high-growth, SaaS, fast-paced technology company.

The annual base salary range for this position for candidates located in Poland is between 225 000 zł-330 000 zł PLN.

Okta offers equity (where applicable), bonus, and comprehensive healthcare coverage and financial benefits including paid time off and parental leave in accordance with our applicable plans and policies.

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The Payments AI/ML Foundation team builds the shared platforms, services, and guardrails that enable product and data teams across Airbnb Payments to deliver trustworthy, efficient, and scalable AI capabilities.

As a Senior Software Engineer on Payments AI/ML Foundation, you will:

• Design and own core platform components that power AI across Airbnb Payments
• Partner with product, infra, data science, and operations to translate ambiguous requirements into robust systems with clear SLAs
• Build tooling and automation that makes AI development safer and faster
• Raise the bar on reliability, performance, and governance for models and agents in production

A typical day for this role will involve envisioning, championing, and supporting the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems.

You will work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for AI/ML models, drive engineering decisions, and quantify impact.

Hands-on productionize and operate AI/ML solution and pipelines at scale, including both batch and real-time use cases.

Lead, mentor, challenge and grow enthusiastic, collaborative AI/ML culture within the organization.

Your expertise should include:

• A degree in Computer Science or equivalent qualification
• 7+ years of industry experience in backend/platform engineering (or equivalent), including ownership of production systems
• Strong programming skills in Python, plus solid data engineering foundations
• Good to have experience with AI frameworks (Langraph, Strand etc), orchestration (Airflow/Kubeflow), and streaming/processing (Kafka/Spark/Ray)
• Proven track record building observability for AI systems (metrics/logging/traces), with automated alerting, dashboards, and SLO management
• Experience designing model governance and safety guardrails (prompt/version control, red-teaming, policy checks, approvals)
• Demonstrated ability to drive performance/cost optimizations (profiling, batching, caching, quantization, autoscaling) in high-traffic environments
• Practical familiarity with vector search/embedding pipelines (indexing strategies, consistency, reindexing, retention)

Our commitment to inclusion and belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions.

All qualified individuals are encouraged to apply.

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As a Staff Software Engineer for Search Quality, you will drive the technical direction of ranking, relevance, evaluation, and quality initiatives across Databricks' next-generation Search product. You'll design and build the systems, models, and evaluation frameworks that ensure our Search stack delivers accurate, high-quality results across diverse multimodal datasets and query patterns.

The impact you will have:

• Lead the technical vision for Search Quality, shaping the ranking architecture, relevance modeling stack, and evaluation systems that power Databricks' next-generation retrieval experiences.

• Identify and solve challenges in ranking, query understanding, and hybrid retrieval , advancing state-of-the-art techniques in vector, keyword, and multimodal search.

• Design and train production-ready ranking and reranking models with strong guarantees around quality, latency, and resource efficiency.

• Partner closely with research, product, and infra teams to define metrics, evaluation methodologies, and experimentation strategies for new retrieval features and model architectures.

• Drive end-to-end engineering efforts , from early prototyping to production rollout , ensuring correctness, reliability, and measurable improvements to relevance.

• Build and operate resilient, low-latency services for ranking, evaluation, and relevance signal processing.

• Champion excellence in ML and search engineering, mentoring teammates and elevating design, code quality, and scientific rigor across the team.

• Shape Databricks' long-term roadmap for retrieval quality, ranking infrastructure, and the foundations for retrieval-driven AI products.

What we look for:

• 10+ years of experience building large-scale search, ranking, recommendation, or ML-driven relevance systems.

• Deep expertise in Search Quality, including ranking models, signals, query understanding, and evaluation methodologies.

• Strong understanding of relevance metrics and evaluation frameworks.

• Familiarity with vector search, keyword search, hybrid retrieval, and embedding-based semantic retrieval.

• Solid foundation in algorithms, data structures, and system design for performance-critical ranking and retrieval systems.

• Proven ability to deliver high-impact technical initiatives with clear business or product outcomes.

• Strong communication skills and ability to collaborate across teams in fast-moving environments.

• Strategic and product-oriented mindset with the ability to align technical execution with long-term vision.

• Passion for mentoring, growing engineers, and fostering technical excellence.

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You will join a team that ships products used by thousands of the world's most sophisticated data and AI organizations. You will drive the vision and roadmap for AI platform product areas and define how customers build, train, deploy, and monitor AI and ML systems on Databricks. You will collaborate across engineering teams to deliver an integrated and powerful path from experimentation to production.

The impact you will have:

• Own the product roadmap for AI platform areas , defining what we build, why, and in what order , to accelerate customer adoption of AI and ML in production.
• Drive strategy for key AI platform capabilities, shaping how enterprises operationalize AI at scale.
• Partner closely with engineering teams to make deeply technical decisions about ML infrastructure , from distributed training architectures to real-time serving systems.
• Represent the voice of the customer by engaging directly with enterprise ML teams, translating their pain points and workflows into platform capabilities that simplify the path to production AI.
• Collaborate with GTM, Solutions Architecture, and Customer Success teams to drive enterprise adoption, shape field enablement, and inform competitive positioning.
• Define pricing, packaging, and commercialization strategy for AI platform features, working with business teams to maximize value capture.
• Grow end-user engagement with Databricks AI tools by identifying adoption bottlenecks and partnering cross-functionally to remove them.

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Location: Remote-USA

The Payments team at Airbnb is responsible for everything related to settling money in Airbnb's global marketplace. As a Senior Machine Learning Engineer for Payments, you will be the catalyst that transforms bold AI innovation into production systems that make Airbnb Payment experience feel effortless and secure.

Responsibilities:

• Spearhead LLM agents, real-time anomaly detectors, and other breakthrough solutions that solve real-world problems and create product magic.

• Collaborate with product, engineering, ops, and data science to spot high-leverage opportunities, refine AI/ML requirements, make principled architecture choices, and measure business value with clear, data-driven metrics.

• Design, train, deploy, and operate large-scale AI applications for both batch and streaming workloads, ensuring low latency, high reliability, and continuous improvement via automated monitoring and retraining loops.

• Mentor and inspire teammates, fostering a collaborative, experimentation-driven environment where cutting-edge research meets production excellence and every engineer is empowered to push AI boundaries at Airbnb.

Your Expertise:

• 5+ years of industry experience in applied AI/ML, inclusive MS or PhD in relevant fields.

• Strong programming (Python/Java) and data engineering skills.

• Proven mastery of modern AI/LLM workflows , prompt engineering, fine-tuning (LoRA, RLHF), hallucination mitigation, safety guardrails, and rigorous online/offline testing to minimize training/inference drift and ensure reliable outcomes.

• Hands-on experience with at least three of the following: PyTorch/TensorFlow, scalable inference stacks, vector search, orchestration/MLOps platforms (Kubeflow, Airflow), large-scale data streaming & processing (Spark, Ray, Kafka).

• Demonstrated success designing, deploying, and monitoring production AI systems , e.g., personalization engines, generative content services , complete with drift/cost/latency monitoring, automated retraining triggers, and cross-functional collaboration that translates ambiguous business needs into measurable AI impact.

• Prior knowledge of AI/ML applications in the Payments domain is highly desirable.

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively led people, and to develop the best products, services, and solutions.

How We'll Take Care of You:

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range: $191,000-$223,000 USD

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We are looking for a Backend AI Engineer to join our team and help build and scale backend systems that power AI Agents for the gaming industry, handling millions of customer support tickets at scale.

In this role, you will work on high-throughput backend services, AI-powered workflows, and retrieval-based systems that support large-scale customer support automation. You will collaborate closely with AI Engineers, ML Engineers, and Product teams to design, build, deploy, and operate reliable backend systems that integrate AI components such as RAG pipelines and AI Agent orchestration.

This is an excellent opportunity to work on production-grade AI systems operating at massive scale, where performance, reliability, and observability are critical.

What You’ll Do:

• Design, develop, and maintain scalable backend services and APIs using Python and frameworks like FastAPI.
• Build and integrate backend workflows supporting AI Agents and customer support automation use cases.
• Develop secure and performant REST APIs for internal and external consumers.
• Integrate AI components such as RAG pipelines, embedding services, and inference APIs into backend systems.
• Work with databases including MongoDB, YugabyteDB, and PostgreSQL to support high-scale, low-latency workloads.
• Containerize applications using Docker and deploy/manage services on Kubernetes (K8s).
• Deploy and operate services on AWS and GCP cloud platforms.
• Implement and maintain CI/CD pipelines, and actively participate in CM/CR release processes.
• Use observability tools such as Grafana, Elasticsearch, and Kibana to monitor system health, debug issues, and improve reliability.
• Write clean, testable, and maintainable code; participate in code reviews and design discussions.
• Troubleshoot and resolve production issues related to scalability, performance, and reliability.

Requirements

• 3–5 years of experience in backend software development.
• Strong hands-on experience with Python and backend frameworks, especially FastAPI.
• Experience building backend APIs and workflow integrations.
• Solid understanding of databases such as MongoDB, PostgreSQL, and YugabyteDB.
• Hands-on experience with Docker and working knowledge of Kubernetes.
• Familiarity with AWS and GCP cloud services.
• Good understanding of CI/CD concepts, build pipelines, and CM/CR release processes.
• Working knowledge of observability and logging systems like Grafana, Elasticsearch, and Kibana.
• Basic to intermediate understanding of AI systems, especially Retrieval-Augmented Generation (RAG).
• Strong problem-solving skills and ability to work effectively in a collaborative team environment.

Good to Have / Plus Skills

• Experience with Jenkins for CI/CD automation.
• Familiarity with MCP concepts and AI orchestration frameworks.
• Hands-on exposure to AI/LLM-based systems, embeddings, or vector search.
• Experience or familiarity with Clojure.
• Knowledge of the Gaming industry or Customer Support domain.
• Experience working in high-scale, high-traffic production systems.

Bonus Points

• Experience building or operating AI Agents in production environments.
• Exposure to distributed systems, event-driven architectures, or message queues.
• Interest in AI reliability, evaluation, and system observability at scale.

Benefits

• Hybrid setup
• Worker's insurance
• Paid Time Offs
• Other employee benefits to be discussed by our Talent Acquisition team in India.

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