You'll design, build, and maintain the indexing infrastructure that powers Firecrawl's core product. You'll handle billions of documents and care about every millisecond of latency and every byte of storage. You'll own the full stack from ingestion to serving. You don't just build one piece , you own the entire pipeline. Ingestion, processing, indexing, ranking, query understanding, and serving. When something breaks at 3am, you know where to look because you built it.

You'll make sure the right content surfaces for the right queries. You'll build and iterate on ranking models, relevance scoring, and query parsing systems that directly impact product quality. You'll tackle freshness, dedup, and incremental indexing. The web changes constantly. You'll build systems that keep our index fresh without re-crawling everything, deduplicate content intelligently, and handle incremental updates at scale without rebuilding from scratch.

You'll run experiments and ship results to production. You design experiments, measure results rigorously, and ship winners to production fast. You don't need someone to tell you what to try next , you have a backlog of ideas and the judgment to prioritize them.

You'll collaborate closely with the team. Work directly with the RL-focused Research Engineer and the engineering team to connect search/IR improvements with model training and the broader product roadmap.

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We are at the forefront of integrating Generative AI and agentic systems into large-scale recommendation pipelines. We are seeking a Senior Applied Scientist to design, build, and optimize ranking and recommendation models that directly impact user engagement across Microsoft’s content ecosystem.

In this role, you will work hands-on with cutting-edge deep learning and LLM-enhanced ranking systems while collaborating closely with engineering and product partners to deliver production-quality solutions at scale.

Responsibilities:

• Design & implement ranking, reranking, and retrieval models using deep learning, LLMs, and advanced recommendation techniques.
• Own end-to-end ML pipelines , feature engineering, model training, offline/online evaluation, and production inference optimization.
• Innovate by applying state-of-the-art methods including LLM-enhanced ranking, contextual bandits, reinforcement learning, and generative recommendation approaches.
• Collaborate cross-functionally with engineering, product, and platform teams to translate research insights into shipped features.
• Contribute to technical direction within the team , propose experiments, identify opportunities, and drive projects from ideation to production.
• Mentor less experienced scientists and engineers, fostering a culture of technical excellence and knowledge sharing.

Qualifications:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 4+ years of industry experience in applied science, machine learning, or deep learning at scale.
• Solid foundation in recommendation systems, ranking models, or search relevance.
• Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) and cloud-scale ML infrastructure.
• Proficiency in Python and data processing tools (Spark, Pandas, or equivalent).
• Track record of shipping ML models to production with measurable user impact.
• Experience with LLM-based ranking, retrieval-augmented generation (RAG), or generative recommendation systems.
• Familiarity with multi-objective optimization, heterogeneous signal fusion, or user modeling.
• Experience with online experimentation (A/B testing, interleaving) and metrics-driven development.
• Publications at top venues (NeurIPS, ICML, KDD, WWW, RecSys, SIGIR).
• Exposure to agentic AI systems or autonomous content curation pipelines.
• Experience with distributed ML training and large-scale data pipelines.

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We are at the forefront of integrating Generative AI and agentic systems into large-scale recommendation pipelines. We are seeking a Senior Applied Scientist to design, build, and optimize ranking and recommendation models that directly impact user engagement across Microsoft’s content ecosystem.

In this role, you will work hands-on with cutting-edge deep learning and LLM-enhanced ranking systems while collaborating closely with engineering and product partners to deliver production-quality solutions at scale.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

• Design & implement ranking, reranking, and retrieval models using deep learning, LLMs, and advanced recommendation techniques.
• Own end-to-end ML pipelines , feature engineering, model training, offline/online evaluation, and production inference optimization.
• Innovate by applying state-of-the-art methods including LLM-enhanced ranking, contextual bandits, reinforcement learning, and generative recommendation approaches.
• Collaborate cross-functionally with engineering, product, and platform teams to translate research insights into shipped features.
• Contribute to technical direction within the team , propose experiments, identify opportunities, and drive projects from ideation to production.
• Mentor less experienced scientists and engineers, fostering a culture of technical excellence and knowledge sharing.

Qualifications:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 4+ years of industry experience in applied science, machine learning, or deep learning at scale.
• Solid foundation in recommendation systems, ranking models, or search relevance.
• Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) and cloud-scale ML infrastructure.
• Proficiency in Python and data processing tools (Spark, Pandas, or equivalent).
• Track record of shipping ML models to production with measurable user impact.
• Experience with LLM-based ranking, retrieval-augmented generation (RAG), or generative recommendation systems.
• Familiarity with multi-objective optimization, heterogeneous signal fusion, or user modeling.
• Experience with online experimentation (A/B testing, interleaving) and metrics-driven development.
• Publications at top venues (NeurIPS, ICML, KDD, WWW, RecSys, SIGIR).
• Exposure to agentic AI systems or autonomous content curation pipelines.
• Experience with distributed ML training and large-scale data pipelines.

#MicrosoftAI Applied Sciences IC4 – The typical base pay range for this role across the U.S. is USD $119,800 – $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 – $258,000 per year.

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

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You will join a cross-functional Product team, Search Intelligence, which is responsible for optimizing ranking and recommendations for users visiting our website.

You'll be part of the broader Data Science team, which operates across cross-functional domain teams - giving you access to shared knowledge, best practices, and collaboration opportunities beyond your domain.

You’ll collaborate daily with Data Engineers, Analysts, Product Managers, and Back-end Engineers.

You’ll report to the Team Lead, Data Science.

Together, we turn data into actionable insights and innovative technology that powers how millions of guests find and book their perfect holiday home.

Our Tech Stack

• Python • Airflow • dbt • AWS (SageMaker, Redshift, Athena) • MLflow

The Ranking challenge at Holidu

Holidu lists over 4 million vacation rental properties. Our ranking and personalization systems determine which of them our 70+ million annual users see, directly impacting search conversion and business results.

What's live today:

• Multi-stage ranking pipeline: Reinforcement-learning-based cold ranking, contextual re-ranking, and personalized recommendations.

• Cold-start models for new properties with limited behavioral data.

• Personalized recommendations based on user browsing patterns.

Some of the hard problems we're solving:

• Multi-objective optimization: Balancing user relevance, conversion probability, and business value.

• Personalization without history: Most users are anonymous or first-time visitors.

• Cold-start: A significant share of our inventory is new each quarter. How do we surface quality properties before we have behavioral data?

Your role in this journey

You'll shape the ranking and recommendation systems that millions of guests rely on to find their holiday home. With access to extensive datasets and modern ML infrastructure, you'll work end-to-end - from identifying opportunities and prototyping new approaches to shipping models to production and measuring their impact.

• Develop high-impact models and improvements for our ranking, recommendation, and personalization systems - with the freedom to explore new, creative approaches.

• Take models from conception to production, continuously monitor their performance, and iterate to enhance accuracy and efficiency.

• Design and run A/B tests as a core part of ranking development; success is measured by successful experiments per quarter and time-to-decision.

• Collaborate closely with Product Managers and Software Engineers to identify, prioritize, and ship ranking improvements.

• Ensure model reliability in production, measured by online/offline agreement, model and data drift KPIs, latency and uptime SLAs, and automated monitoring coverage.

• Advance our MLOps practices with CI/CD pipelines, retraining workflows, lineage tracking, and documentation.

• Demonstrate leadership in data science projects by driving technical direction, scoping initiatives, and guiding the team's prioritization and project execution.

Your backpack is filled with

• 5+ years of experience as a Data Scientist, with a proven track record of applying ML models to solve real business problems.

• Experience working on ranking models or recommender systems is a strong advantage.

• A degree in Machine Learning, Computer Science, Mathematics, Physics, or a related field.

• Strong foundations in statistics, predictive modeling, and machine learning techniques, with hands-on experience using Python and SQL.

• Experience with Airflow and dbt is a plus.

• Solid understanding of business operations and the ability to translate data insights into clear, actionable outcomes.

• A collaborative mindset and enthusiasm for using data to build world-class products that make a real impact.

• AI Proficiency: You are comfortable using AI to enhance coding, planning, and monitoring. This includes successfully integrating AI tools (such as Claude code, Codex, Copilot, etc.) into your workflow and teaching others to use them efficiently.

Our adventure includes

• Impact: Shape the future of travel with products used by millions of guests and thousands of hosts. At Holidu ideas become products, data drives decisions, and iteration fuels fast learning. Your work matters - and you’ll see the impact.

• Learning: Grow professionally in a culture that thrives on curiosity and feedback. You’ll learn from outstanding colleagues, collaborate across disciplines, and benefit from mentorship, and personal learning budgets - with a strong focus on AI.

• Great People: Join a team of smart, motivated and international colleagues who challenge and support each other. We celebrate wins and keep our culture fun, ambitious and human. Our customers are guests and hosts - people we can all relate to - making work meaningful and energizing.

• Technology: Work in a modern tech environment. You’ll experience the pace of a scale-up combined with the stability of a proven business model, enabling you to build, test, and improve continuously.

• Flexibility: Work a hybrid setup with 50% in-office time for collaboration, and spend up to 8 weeks a year from other inspiring locations. You’ll stay connected through regular events and meet-ups across our almost 30 offices.

• Perks on Top: Of course, we also offer travel benefits, gym discounts, and other perks to keep you energized - but what truly sets us apart is the chance to grow in a dynamic industry, alongside amazing people, while having fun along the way.

Need a sneak peek? Check out the adventure that awaits you on Instagram @lifeatholidu and dive straight into the world of Tech at Holidu for more insights!

Want to travel with us?

Apply online on our careers page! Your first travel contact will be Lucia from HR.

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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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