Forward Deployed Engineer

GAQ427R208 We're looking for a Recruiter with experience driving recruitment in the EMEA region for our Field Engineering & Forward Deployed Engineering teams, to help us identify and hire great people for Databricks as we continue our expansion. You will conceive practical and creative ways to identify leading talent and invest considerable time activating and engaging passive candidates. You will use data to set benchmarks and metrics to create targeted recruiting strategies while partnering with leadership to build inspired teams. You will report directly to the Manager, EMEA Talent Acquisition. This is a fixed-term position for a period of 12 months, with the possibility of extension. The impact you will have Own full-cycle hiring for entry to mid-level Field Engineering roles across EMEA — Associate and mid-level Solutions Architects, Solutions Engineers, Delivery Solutions Architects, and similar. Run creative, high-quality outbound sourcing: build target lists, write outreach that people actually reply to, and keep a healthy pipeline of engaged passive candidates. Build market maps and talent pools by market, skill and competitor, and keep them warm so we can move quickly when headcount opens. Partner with hiring managers and senior recruiters on intake, calibration and candidate feedback, and keep them informed with clear, regular updates. Give every candidate a great experience — fast, honest communication and a well-run process from first message to offer. Keep our data clean and useful: accurate ATS hygiene, reliable reporting on funnel and sourcing performance, and insight on what channels are actually working. Contribute to how we source: test new tools, channels and outreach approaches, and share what you learn with the wider team. What we look for Experience sourcing and recruiting in-house or in agency, ideally within software, SaaS, or the Data and AI space. Demonstrable outbound sourcing ability — you can talk us through how you find and

Perplexity is seeking an experienced Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities Relentlessly push search quality forward—through models, data, tools, or any other leverage available Architect and build core components of our search platform and model stack Train and evaluate retrieval, ranking and classification models, including LLMs Deploy models - from boosting to LLMs - in a scalable and performant way Build and optimize RAG pipelines for grounding and answer generation Collaborate with Data, AI, Infrastructure and Product teams to ensure fast and high quality delivery Qualifications Deep understanding of search and retrieval systems, including quality evaluation principles and metrics Proven track record with large-scale search or recommender systems Self-driven, with a strong sense of ownership and execution Minimum of 5 years of working on search or recsys-related projects

Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. Our work spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective. We control the full stack: the models, the agent harnesses, and the search infrastructure underneath. That gives us the freedom to develop new approaches across all three training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work. Responsibilities Push search and agent quality forward through improvements to models, training data, tools, and system design. Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion. Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively. Design and build agent harnesses around the tools, context management, execution environments, and orchestration that support reliable work over many steps. Improve retrieval and ranking models and the search interfaces agents use to find and assess information. Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements. Own experiments end to end, from a clear hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost. Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production. Qualifications A strong track record of building and shipping ML systems, with deep experience in one or more of LLM post-training, reinforcement learning, search and retrieval, or agent systems. Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems. Experience designing rigorous evaluations

Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. We are looking for a strong ranking generalist who can own ambiguous problems end to end and brings exceptional depth in either neural ranking or production ranking systems. Responsibilities Push search quality forward through models, data, evaluation, infrastructure, or any other leverage available. Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely. Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate. Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring. Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity. Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome. Qualifications Deep understanding of search or recommender systems and their evaluation. Proven ownership of a large-scale production ranking system or a substantial class of quality problems. Strong machine learning and software engineering skills across data, models, serving, and monitoring. Ability to drive ambiguous, cross-team problems without continuous task decomposition. Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.