This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role is focused on architecting and scaling the ranking systems that power search, recommendations, and personalization across a high-traffic e-commerce platform. - Design multi-task, multi-objective models that optimize for long-term value, relevance, and user engagement, while leveraging LLMs to enhance features and recall. - Partner closely with engineers, product managers, and data teams to lead the development of production-grade ML systems. - Ensure low-latency serving and mentor other ML engineers. - Combine cutting-edge research with practical implementation, influencing user experience, revenue, and retention. - Contribute to both technical strategy and operational excellence in large-scale machine learning systems. - Architect and implement the ranking backbone that unifies search, personalization, ads, and merchandising into a single adaptive platform. - Design and develop multi-task learning models (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk. - Build value-aware, long-horizon objective functions and uplift/causal models to optimize incremental revenue, retention, and user engagement. - Own low-latency inference pipelines including re-ranking, diversity and quality constraints, and safe exploration strategies. - Advance evaluation practices through online experiments, counterfactual analyses, and attribution pipelines to measure long-term impact. - Collaborate with cross-functional teams including product, ads, infrastructure, and design to translate business goals into ML policies and measurable ROI. - Mentor and guide ML engineers, fostering expertise in ranking, causal inference, and scalable serving systems. Qualifications - 5+ years of experience applying ML at scale, with at least 3 years in technical leadership roles improving ranking or recommendation systems. - Proven experience with multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience. - Strong coding skills in Python and data fluency using SQL/Pandas; experience with XGBoost and deep learning frameworks such as TensorFlow or PyTorch. - Solid understanding of low-latency serving architectures, feature stores, caching, vector/lexical retrieval, and re-ranking systems. - Expertise in multi-task learning, calibration, counterfactual evaluation, uplift/causal modeling, or contextual bandits is preferred. - Hands-on experience leveraging LLMs for feature enrichment, long-tail recall, or reasoning-rich context in ML pipelines. - Excellent analytical, problem-solving, and cross-functional communication skills. - Experience with remote-first collaboration and asynchronous alignment across teams and time zones. Benefits - Competitive base salary, with ranges depending on U.S. location: $173,000–$219,000. - Equity grants for new hires and annual refresh grants. - Comprehensive medical, dental, and vision coverage. - Flexible PTO and remote-first work culture. - Opportunities for mentorship, professional development, and research contributions. - Access to cutting-edge ML infrastructure and projects impacting millions of users. - Inclusive and collaborative environment with a focus on innovation and learning.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers a unique opportunity to lead the development of next-generation machine learning systems for real-time dispatch and logistics optimization. You will design and implement production-grade ML pipelines, optimization models, and decision-making services that directly impact operational efficiency and customer satisfaction. The position combines hands-on technical engineering with team leadership, mentoring, and cross-functional collaboration across Product, Operations, and Data Engineering teams. You will have ownership over both modeling and deployment, ensuring robust, scalable, and automated ML solutions. The environment is dynamic and data-driven, providing room to experiment, simulate, and iterate on complex supply-demand and service optimization problems. This fully remote role within the U.S. allows you to drive innovation in a high-impact operational setting. - Architect and deploy end-to-end Python-based ML services for batch and streaming workflows that integrate predictive models into real-time dispatch decisions. - Build, extend, and validate optimization and machine learning models (e.g., gradient-boosting, deep learning, OR-Tools) to balance service-level objectives and operational costs. - Simulate and quantify short- and long-term trade-offs in time-horizon dispatch scenarios. - Operationalize model training, validation, A/B testing, and monitoring using cloud-native tools such as SageMaker and Airflow. - Mentor and guide a small squad of ML engineers, ensuring high-quality code and robust workflows. - Collaborate cross-functionally with Product, Operations, and Data Engineering teams to align ML solutions with business objectives. - Continuously instrument performance metrics, identify failure modes, and implement improvements to optimize NPS, cost efficiency, and overall system performance. Qualifications - 6+ years of experience in machine learning engineering with ownership of production ML systems. - Expert-level Python programming skills and experience designing cloud-native ML pipelines (AWS preferred). - Hands-on experience with optimization techniques (Mixed Integer Programming, Linear or Stochastic Optimization) and modern ML frameworks (XGBoost, PyTorch). - Strong SQL skills, feature-store design knowledge, and a strong data-quality mindset. - Proven ability to translate complex business requirements into mathematically rigorous experiments and ML solutions. - Experience designing, deploying, and monitoring scalable ML models and services in production. - Nice-to-have: experience in dispatch, logistics, supply-demand marketplaces, Monte-Carlo simulations, multi-agent systems, hierarchical reinforcement learning, or balancing short-term vs. long-term KPIs. Requirements - 6+ years of experience in machine learning engineering with ownership of production ML systems. - Expert-level Python programming skills and experience designing cloud-native ML pipelines (AWS preferred). - Hands-on experience with optimization techniques (Mixed Integer Programming, Linear or Stochastic Optimization) and modern ML frameworks (XGBoost, PyTorch). - Strong SQL skills, feature-store design knowledge, and a strong data-quality mindset. - Proven ability to translate complex business requirements into mathematically rigorous experiments and ML solutions. - Experience designing, deploying, and monitoring scalable ML models and services in production. - Nice-to-have: experience in dispatch, logistics, supply-demand marketplaces, Monte-Carlo simulations, multi-agent systems, hierarchical reinforcement learning, or balancing short-term vs. long-term KPIs. Benefits - Competitive base salary range: $150,000–$200,000 USD, based on experience and location. - Fully U.S.-based remote work with flexible hours. - Comprehensive health, dental, vision, disability, and life insurance plans. - 401(k) retirement plan with company match. - Flexible time off, paid sick leave, and 10+ paid holidays annually. - Parental and family support benefits. - Bonus and incentive programs. - Opportunities for professional growth, mentorship, and leadership within a collaborative, inclusive environment.

• Build, train, and ship ML models for identity verification use cases such as biometric matching, liveness / anti-spoofing, identity document processing (OCR/extraction), and fraud detection (team assignment based on experience). • Prepare large, noisy datasets: ingestion, validation, cleaning, deduplication, labeling strategy, and dataset QA to improve model performance and reliability. • Design experiments, evaluation protocols, and success metrics (offline and online), iterate based on measurable business impact (detection rates, fraud losses, false positives). • Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, and cost controls. • Productionize models as resilient services and libraries in Python; collaborate with platform teams on APIs, latency and observability. • Contribute to the transformation of our IDV engine: modernizing legacy components, improving modularity, and raising quality, performance, and maintainability. • Work closely with Product, Customer Success, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, and reliability requirements. • Support other engineers through design reviews, code reviews, and knowledge sharing; help raise the technical bar across the team.

• Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases. • 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 machine learning models, drive engineering decisions, and quantify impact. • Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases. • Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep. • Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.