This isn't a pure research role and it isn't a pure backend role. You sit at the intersection: you understand how the systems work deeply enough to improve them, and you care about how they feel to use obsessively enough to make them great. At a 26-person company, the gap between research and shipped product is exactly one person. You're that person.

Salary Range: $180,000–$290,000/year (Range shown is for U.S.-based employees. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)

Equity Range: Up to 0.15%

Job Type: Full-Time

Experience: 3+ years in applied RL, ML engineering, or model training , with production systems

Visa: US Citizenship/Visa required for SF; N/A for Remote

You'll be responsible for how Firecrawl's search endpoint feels to integrate, use, and build on. That means response format, latency, error handling, pagination, filtering , every surface a developer touches. You're the person who notices when something is confusing before a user files a GitHub issue about it.

You'll dogfood relentlessly. You build things with the API before you ship them. You feel the friction before your users do. You read every GitHub issue, every Discord thread, every support ticket that touches search , not because someone asked you to, but because that's where the product signal lives.

You'll translate research into product decisions. You work closely with the Search/IR and RL Research Engineers. You understand their work well enough to make good product calls about what to prioritize, what to expose to users, and what to keep under the hood. You ask good questions. You push back when something technically elegant would make the API worse.

You'll run fast product experiments. You form a hypothesis about what would make search better for developers, instrument it, ship it, measure it, and decide quickly. You're comfortable making calls with imperfect data because waiting for perfect data means shipping nothing.

You'll raise the bar on developer experience. Firecrawl's users are technical. They have high standards. They notice when response formats are inconsistent, when error messages are unhelpful, when documentation doesn't match behavior. You notice too , and you fix it before they have to ask.

What We're Looking For:

• Obsessive about developer experience. You think about DX the way a designer thinks about pixels. Latency, response structure, error messages, API ergonomics , these things matter to you on a visceral level. You've built APIs that developers loved and you know the difference between an API that works and one that delights.

• Speaks both product and engineering fluently. You can read a ranking algorithm and understand its implications for the search experience. You can write the API spec and implement it yourself. You don't need a PM to tell you what matters or an ML engineer to explain why a retrieval change is significant. You connect those dots on your own.

• Hands-on builder who ships. You write code. You own features from design to deployment. You're comfortable with ambiguity and you don't need a perfectly scoped ticket to make progress. You ship something, learn from it, and iterate.

• Has a feel for search as a product. You've thought seriously about what makes search good , not just fast or accurate, but genuinely useful. You understand the difference between recall and precision and why developers care about both. You have intuitions about query understanding, result ranking, and when semantic search beats keyword search , and you've built products that put those intuitions to work.

• Brings production instincts. You've operated systems under real load. You know what breaks first, how to instrument what matters, and how to make good latency/quality tradeoffs. You're not just building features , you're building infrastructure developers depend on.

What We're NOT Looking For:

• Great engineers who don't care about DX. If you build technically excellent systems but think API ergonomics and documentation are someone else's problem, this isn't the role. The product experience is part of the job , not an afterthought.

• People who need a PM. There's no product manager between you and the work. You define what good looks like, you decide what to prioritize, and you own the outcome. If that's uncomfortable, you'll struggle here.

• Specialists who only work on one layer. If you're only interested in backend systems and tune out when the conversation shifts to how something is exposed to developers , or vice versa , this won't be a fit. This role requires you to hold both.

• Slow shippers. The research team will produce improvements faster than a slow product cycle can absorb them. We need someone who can take something from 'this ranking model is better' to 'this is live in the API with docs and an example' in days, not sprints.

• People who don't use the product. If you're not the kind of engineer who builds side projects with APIs like ours, reads the docs critically, and notices when something feels off , you'll miss the signal that makes this role work.

A Note On Pace:

We operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings , but this role probably isn't for you.

Benefits & Perks:

• Salary that makes sense , $180,000–$290,000/year, based on impact, not tenure

• Own a piece , Up to 0.15% equity in what you're helping build

• Generous PTO , 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge

• Parental leave , 12 weeks fully paid, for moms and dads

• Wellness stipend , $100/month for the gym, therapy, massages, or whatever keeps you human

• Leave

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Responsibilities: Contribute to defining a product area by understanding user needs and behaviors, writing product requirements, and supporting end-to-end development, launches, and iterations. Identify long-term investment opportunities and drive business impact across the organization. Work through roadblocks and help teams ship quickly and iteratively. Translate business goals into product strategy, user experience, and technical requirements in collaboration with UX and engineering. Define success metrics, support experimentation, and use data to guide decisions. Dive into data to understand user behavior, product performance, and new opportunities. Communicate clearly and collaborate effectively across cross-functional teams. Model our culture and values in daily work.

Qualifications: Required Qualifications: Bachelor’s Degree AND 5+ years experience in product/service/program management or software development OR equivalent experience. Preferred Qualifications: Bachelor’s Degree AND 8+ years experience in product/service/program management or software development OR equivalent experience. Experience with AI. Experience with Grounding, Ranking, Query Understanding. Experience in the Search space.

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As a Senior Staff, you will design and build a large-scale system that powers end-to-end search relevance at scale. You’ll work across multiple areas of the stack, including query understanding, retrieval, ranking, features, measurement, LLM-based answers, RAG etc, partnering deeply with product, org leads, and other XFN to deliver reliable, high quality systems that can help Reddit Search scale to billions of users.

Key responsibilities include:

Contribute to advancing Reddit's Search and Recommendations products by designing AI-driven Search experience prioritizing seamless and delightful user experience.

Deeply understand the Reddit search product and drive the vision for the search relevance team.

Enhance core search retrieval and ranking, design and implement new search engine features, build and scale search indexes, develop and test new pipeline components.

Collaborate across disciplines and with ML, Product, Infrastructure, and DS teams at Reddit to find technical solutions to complex challenges.

Mentor and guide senior and staff engineers in the team.

Influence org-wide technical direction and partner closely with senior leadership to shape the long-term roadmap.

We are looking for someone with 10+ years of industry experience with deep expertise in large-scale search and recommendation systems. You should have a proven ability to identify key opportunities, define roadmaps and drive scalable improvement in search relevance. Strong experience in building and deploying large-scale ML models using frameworks such as PyTorch or Tensorflow is also required.

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Key responsibilities include:

• Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation
• Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience
• Contribute to building a robust framework for evaluating search ranking improvements - both offline and online

The ideal candidate will have a strong understanding of computer science fundamentals, experience applying LLM to search relevance, and contributions to well-used open-source projects.

At Databricks, you'll have the opportunity to shape the future of AI-driven products, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts.

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Key Responsibilities: Drive the development and deployment of ML-based search and discovery relevance models and systems integrated with Databricks' products and services. Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration. Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience. Contribute to building a robust framework for evaluating search ranking improvements - both offline and online.

What We’re Looking For: BS+ (M.S. or PhD preferred) in Computer Science, or a related field. 10+ years experience developing search relevance systems at scale in production or in high-impact research environments. Experience applying LLM to search relevance. Experience in one or more of the following: query understanding, NLP, text mining, recommendations, personalization, discovery, conversational AI. Strong understanding of computer science fundamentals. Contributions to well-used open-source projects.

Why Join Us? At Databricks, we are building state-of-the-art AI solutions that redefine how users interact with data and our products. You’ll have the opportunity to shape the future of AI-driven products at Databricks, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts. If you're excited about pushing the boundaries of AI in real-world applications, we’d love to hear from you!

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Key responsibilities include:

• Developing and deploying ML-based search and discovery relevance models and systems integrated with Databricks' products and services.
• Designing and implementing automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration.
• Collaborating with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience.
• Contributing to building a robust framework for evaluating search ranking improvements - both offline and online.

Requirements include:

• A Bachelor's degree in Computer Science or a related field, with a Master's or PhD preferred.
• 5+ years of experience developing search relevance systems at scale in production or in high-impact research environments.
• Experience applying LLM to search relevance.
• Strong understanding of computer science fundamentals.
• Contributions to well-used open-source projects.

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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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