Role Description At Rackner, we build systems where advanced technologies move beyond prototypes and into real-world operational use. We are seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment. This is not a research role. This is where models become reliable, deployable, and auditable systems. You will operate at the intersection of: - machine learning - cloud-native infrastructure - distributed systems …and ensure AI/ML systems are production-ready in environments where reliability and performance matter. What You’ll Do - Own the ML Lifecycle (End-to-End) - Build and operate production-grade ML pipelines - Orchestrate workflows using Kubeflow, Airflow, or Argo - Implement model versioning, lineage, and reproducibility standards - Operationalize AI/ML Systems - Deploy models into secure and constrained environments - Transition workflows from experimentation → containerized pipelines → production systems - Enable both batch and real-time inference architectures - Engineer for Reliability - Design systems for reproducibility, auditability, and stability - Monitor model performance and system health using Prometheus, Grafana, OpenTelemetry - Detect and resolve issues such as model drift and system degradation - Build Cloud-Native ML Infrastructure - Deploy and manage Kubernetes-based ML workloads - Containerize pipelines using Docker - Support scalable training and inference workflows - Establish Data Discipline - Support feature engineering and dataset preparation - Implement data versioning and governance practices (e.g., lakeFS) - Apply metadata and data management standards - Create Repeatable Systems - Develop runbooks, playbooks, and documentation - Build systems that are operationally sustainable and transferable Qualifications - Experience deploying ML systems into production environments - Strong programming skills in Python - Hands-on experience with: - ML pipeline tools (Kubeflow, Airflow, Argo) - Experiment tracking tools (MLflow, ClearML) - Experience with Kubernetes and containerized systems (Docker) - Familiarity with CI/CD pipelines - Understanding of distributed systems and scalable architectures - Experience working with: - LLMs or transformer-based models - Computer vision systems (YOLO, Faster R-CNN) - Focus on deployment and integration, not pure research Mindset - Systems thinker who prioritizes reliability over novelty - Comfortable operating in complex, evolving environments - Focused on delivering real-world outcomes Clearance Requirements - Active TS/SCI clearance strongly preferred - Candidates with an active Secret clearance may be considered and supported for upgrade - Candidates without an active clearance must be: - U.S. citizens - Eligible to obtain and maintain a clearance - Able to work in a CAC-enabled or secure environment Note: Start timelines and work scope may vary depending on clearance status and program requirements. Benefits - 100% covered certifications & training aligned to your role - 401(k) with 100% match up to 6% - Highly competitive PTO - Comprehensive Medical, Dental, Vision coverage - Life Insurance + Short & Long-Term Disability - Home office & equipment plan - Industry-leading weekly pay schedule Company Description Rackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We are an energetic, growing team focused on solving complex problems through: - Distributed systems - DevSecOps - AI/ML - Cloud-native architecture Our approach is cloud-first, cost-effective, and outcome-driven, delivering systems that scale and perform in real-world environments. If you’re an engineer who wants to move from building models → owning production systems, we’d like to connect.

Privacy Manager - Machine Learning Assessments: 25-06388 San Francisco, CA 94103 Primary Skills: Compliance (Expert), GDPR/CCPA (Advanced), PIA/DPIA (Advanced), Privacy-by-Design (Advanced), Program Management (Expert) Contract Type: W2 Duration: 12+ Months Location: Remote Pay Range: $65 - $70 per hour on W2 Job Summary: As a Privacy Manager, you will lead the completion of privacy impact assessments and ML privacy reviews. You will work closely with various stakeholders to document and mitigate privacy risks associated with new features and services. Your goal is to ensure compliance with global data protection laws through effective privacy strategies and documentation. Key Responsibilities: - Collaborate with technical teams to understand data flows and processing activities. - Conduct privacy and ML reviews, identifying risks and ensuring appropriate mitigations. - Develop and refine privacy documentation, ensuring it's prepared for review and approval. - Manage the progress of multiple documents and ensure their timely completion. - Coordinate with internal teams for the delivery of privacy documentation and compliance monitoring. Must Have Skills: - Strong knowledge of global data protection laws (e.g., GDPR, CCPA). - Proven experience in conducting PIAs and DPIAs. - Exceptional communication skills for translating complex privacy and technical concepts. Industry Experience Required: - Minimum 5 years in privacy operations, compliance, or data governance within technology or enterprise environments.

Role Description As a Principal Machine Learning Scientist, you will be the technical anchor for our core discovery engine. You will lead the machine learning strategy and execution that powers how millions of users search for, discover, and organize their complex travel itineraries. This is a high-impact role bridging the gap between cutting-edge AI research and production-grade engineering, directly influencing multi-objective business outcomes like user engagement, booking conversion, etc. You will tackle complex, ambiguous problems at the intersection of deep multi-task ranking, sequential user modeling, and graph-based travel recommendations. If you are passionate about building state-of-the-art AI systems and mentoring a high-performing team of scientists, this role is for you. What You'll Do - Technical Leadership & Execution: Drive the technical roadmap for Search, Retrieval, Ranking, and Recommendation systems within the Trips vertical. Translate high-level business goals into concrete ML architectures and scalable production systems. - Advanced Algorithm Innovation: Design, prototype, and scale next-generation recommendation and ranking models. Solve complex, non-linear travel journeys by utilizing sequential recommenders, representation learning, and deep multi-objective frameworks. - System Architecture & Scalability: Oversee the deployment of low-latency, high-throughput retrieval and ranking pipelines (e.g., multi-stage retrieval, vector search) capable of processing billions of travel data points (reviews, photos, bookings, user intent) in real-time. - Cross-Functional Collaboration: Partner closely with Product Managers, Engineering Leads, and Data Science peers to optimize multi-task business objectives simultaneously. Act as the primary technical authority for ML initiatives within the Trips vertical. - Talent Multiplier: Mentor and coach senior and mid-level ML scientists. Foster a culture of technical excellence, driving best practices for MLOps, rigorous A/B testing, data privacy, and code quality. Qualifications - Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics, or a highly quantitative field. - 8+ years of industry experience developing and deploying large-scale ML models in a production environment, with a proven track record of shipping systems at the scale of millions of active users. Requirements - Deep theoretical and practical knowledge in the following areas: - SOTA Retrieval & Ranking: Practical experience with Multi-Task Learning (MTL), Multi-gate Mixture-of-Experts (MMoE), or similar architectures optimized for multi-objective optimization. - Sequential & Temporal Modeling: Hands-on experience building sequential recommendation systems that capture real-time user session dynamics and long-term historical preferences. - Advanced Representation Learning: Deep understanding of embedding generation, deep semantic retrieval, and multi-modal representation learning. - Technical Stack: Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands-on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP). - Desired: - Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel entities (e.g., users, destinations, itineraries, points of interest). - Agentic AI & Generative AI: Familiarity with Agentic AI frameworks, LLM-driven reasoning, or autonomous planning agents to enhance conversational search and automated itinerary generation. - Experience working in E-commerce, Travel Tech, or Two-Sided Marketplaces, specifically handling non-linear user journeys and highly constrained inventory (e.g., hotel availability, tour timings). - A strong track record of academic or industry contributions, including publications in top-tier AI/IR conferences (e.g., SIGIR, KDD, RecSys, NeurIPS) or open-source ML contributions. Benefits - Competitive compensation packages (routinely benchmarked against the latest industry data), including base salary and annual bonuses. - “Work your way” with flexibility to suit your lifestyle. Tripadvisor Group takes a remote-friendly approach to collaboration across a worldwide team, with the option to join on-site as often as you’d like or as required by your team. - Flexible schedule. Work-life balance is ingrained in our culture by design. Trust and accountability make it work. - Donation matching. Give back? Give more! We match qualifying charitable donations annually. - Tuition assistance. Want to level up your career? We love to hear it! Receive annual support for qualified programs. - Lifestyle benefit. An annual benefit to spend on yourself. Use it on travel, wellness, or whatever suits you. - Travel perks. We believe that travel is employee development, so we provide discounts and more. - Employee assistance program. We’re here for you with resources and programs to help you through life’s challenges. - Health benefits. We offer great coverage and competitive premiums. - Generous referral scheme. Help us grow and be rewarded with generous awards for referring successful candidates.

• Design, develop, and deploy machine learning solutions and feature engineering pipelines. • Configure, test, debug, deploy, document, and maintain ML pipelines, models and feature engineering modules while adhering to specific development best practices and quality standards. • Work closely with data scientists, data engineers, and solution architects to develop technical design specifications for ML programs, focusing on efficient feature engineering and model deployment. • Analyze large-scale datasets and validate the proposed ML solutions with both the architectural design and the business needs, ensuring model performance meets target metrics. • Responsible for troubleshooting and issue analysis across the ML stack, including feature pipelines, model training, inference, and model monitoring, as well as coding, testing, and implementing model enhancements. • Demonstrate a strong understanding of supervised, unsupervised, ensemble, and deep learning algorithms to design and implement effective ML solutions, with experience in feature engineering, model evaluation, and continuous performance optimization to meet business targets. • Implement and maintain MLOps practices including experiment tracking, model versioning, A/B testing, and automated retraining pipelines. • Thrive in a fast-paced agile development environment, driving iterative model improvements. • Implement and maintain data governance and model monitoring frameworks to ensure model reliability, fairness, and compliance with business standards. • Available to support/unblock planned model deployments and retraining cycles during off hours. • Contribute to the evolution of our ML architecture, with a focus on MLOps principles and emerging technologies for feature stores.