Infrastructure Operations Analyst in Belgrade

We're building the future of Speech Intelligence at Speechmatics, and we need someone who can own the technical engine behind how customers discover, adopt, and grow with our platform. This is a hands-on backend engineering role, where your work directly shapes the journey from a customer's first interaction with our website to their first meaningful moment of value. If you thrive shipping, iterating, and making a measurable commercial difference, then we’d love to hear from you. What you'll do Own the backend development and ongoing health of the Speechmatics website and self-serve portal Build and maintain new custom components from scratch, turning design specifications into reliable, performant, production-ready code Manage Vercel deployments and backend infrastructure, keeping the live site fast, stable, and technically sound Implement UI design changes end-to-end, bridging the gap between what's designed and what goes live Write Python-based queries to surface insights across the self-serve portal and support data-driven decisions across the team Identify and act on opportunities to improve conversion across the self-serve funnel through well-timed, well-designed product experiences Collaborate closely with product, design, and data teams in a cross-functional, async-friendly way, working closely together to remove blockers and unlock opportunities Own the ongoing improvement of site performance, loading speed, and technical reliability What you'll need Proficiency in JavaScript and TypeScript for website backend development, with a track record of shipping and maintaining production code Hands-on Python experience, particularly for data queries Demonstrable experience with LLM and AI-first development workflows, tools like Claude, Cursor, or similar Experience in a product-led growth or self-serve SaaS environment, with a clear understanding of conversion funnels and how engineering choices affect them The ability to operate

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 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

We're building the future of Speech Intelligence at Speechmatics, and we need someone who can own the technical engine behind how customers discover, adopt, and grow with our platform. This is a hands-on backend engineering role, where your work directly shapes the journey from a customer's first interaction with our website to their first meaningful moment of value. If you thrive shipping, iterating, and making a measurable commercial difference, then we’d love to hear from you. What you'll do Own the backend development and ongoing health of the Speechmatics website and self-serve portal Build and maintain new custom components from scratch, turning design specifications into reliable, performant, production-ready code Manage Vercel deployments and backend infrastructure, keeping the live site fast, stable, and technically sound Implement UI design changes end-to-end, bridging the gap between what's designed and what goes live Write Python-based queries to surface insights across the self-serve portal and support data-driven decisions across the growth team Identify and act on opportunities to improve conversion across the self-serve funnel through well-timed, well-designed product experiences Collaborate closely with product, design, and data teams in a cross-functional, async-friendly way, working closely together to remove blockers and unlock opportunities Own the ongoing improvement of site performance, loading speed, and technical reliability What you'll need Proficiency in JavaScript and TypeScript for website backend development, with a track record of shipping and maintaining production code Hands-on Python experience, particularly for data queries Demonstrable experience with LLM and AI-first development workflows, tools like Claude, Cursor, or similar Experience in a product-led growth or self-serve SaaS environment, with a clear understanding of conversion funnels and how engineering choices affect them The ability to o

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.

P-1415 At Databricks, we are passionate about helping data teams solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, Databricks has started a multi-year journey to build the best Data Intelligence Platform. For this we are building on top of a great foundation but our goal is to go further to build dramatically better products. We want to revisit every component to provide our customers with the fastest, easiest to use and most secure data platform for all their data workloads. As a software engineer, you will join as a founding member of not only the Belgrade site, but really as a founding team for our multi-year journey to achieve our Lakehouse vision. You will be involved in the entire development cycle and exemplify all core Databricks values. The impact you'll have: Our backend teams span many domains across our essential service platforms. For instance, you might work on challenges such as: Problems that span from product to infrastructure including: distributed systems, at-scale service architecture and monitoring, workflow orchestration, and developer experience. Build reliable, secure and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, GCS, Azure Blob Store. Develop product features that empower our customers to easily store and access their data. What we look for: BS (or higher) in Computer Science, or a related field 5+ years of production level experience in one of: Java, Scala, C++, or similar language Experience developing large-scale distributed systems Experience working on a SaaS platform or with Service-Oriented Architectures Knowledge of SQL About Databricks Databricks is the D

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

Job Overview Wargaming is looking for a Performance QA Team Lead to join our World of Warships PC team. In this role, you’ll lead the Performance QA team, drive performance testing and automation using Python, and help ensure the game delivers a smooth and stable experience for players. We’re looking for a technical leader with a strong and broad background in performance engineering, who can combine hands-on expertise with team leadership and help us continuously improve our performance testing practices. Reports to Head of QA What will you do? Lead the Performance QA team: task planning, coordination, mentoring, and technical guidance Define and maintain performance testing strategy, coverage, baselines, thresholds, and regression criteria Investigate performance regressions, identify bottlenecks, and drive root-cause analysis across client, backend, and system-level components Drive performance testing and automation across game client, backend services, network interactions, and system-level scenarios Create and maintain test infrastructure, tools, and frameworks for QA team Maintain documentation for performance tests, tools, and testing processes Collaborate with development and other teams to analyze performance results and drive improvements Integrate and maintain performance testing within CI/CD pipelines Participate in architectural and technical design reviews from a QA perspective What are we looking for? 4+ years of experience in Performance QA, Performance Engineering, or related areas Experience in leading QA teams or technical leadership 2+ years of professional experience with Python Strong understanding of performance engineering principles and the ability to investigate performance issues across complex systems Experience in at least one of the following areas: client performance, backend/load performance, distributed systems, or system-level performance analysis Upper-Interme

Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information. Internship program: 12 - 24 weeks, full-time, part-time, in-person in the Belgrade office. Responsibilities: Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers. Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models. Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks. Support deployment and monitoring of search and ranking models in a scalable and performant way. Help build and iterate on RAG pipelines for grounding and answer generation. Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML. Qualifications: Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems. Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects. Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required. Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required. Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment Experience with Rust will be a plus What we're looking for: You’ve built something you’re proud of: Not just a class assignment - we want to see a project y

P-1439 As Applied ML/AI Engineer at Databricks, you will define and create the Agentic Machine Learning revolution. You will help world’s largest companies build and deploy massive classical machine learning solutions (such as personalized recommendations, fraud detection, dynamic pricing, forecasting) fully using Databricks’ agents and harnesses. After 30 years of painfully slow progress, expensive knowledge acquisition and tedious processes in classical ML, the agentic era has ushered innovation, scale and visibility for all: from the most junior data scientists and machine learning engineers to the world class experts. You will work on Databricks’ Genie Code, an agent and an ML “exoskeleton” that is authoring ML, training and deploying models, monitoring and self-healing production ML, all at the top-expert level. Additionally, you will develop ML solutions that under the hood address the most pressing questions non-technical business people have today: predictive and what-if questions. Your work is a top priority for Databricks and you will report to a Principal Software Engineer (a Director level position). The impact you will have: Build agentic end-to-end systems in a small, highly international team of experienced engineers in a flat and agile organization. Build solutions for people like you: software engineers, ML engineers, data engineers, data scientists and MLOps engineers. Own our applied ML investment by engaging with engineering and product teams across the company. Drive the development and deployment of state-of-the-art ML/AI models and systems that directly impact the capabilities and performance of Databricks’ products, infrastructure, and services. Architect and implement robust, scalable ML infrastructure, including model training and serving components to support seamless integration of AI/ML models into production environments. Work on novel forecasting techniques. Possibilities to contribute to the broader AI community by presenting a

Perplexity is looking for a highly skilled Senior or Expert Systems Engineer to join our Search Core team. This role is critical to building next-generation search products and technologies. You will help drive key decisions around the architecture, design, and implementation of foundational components in our technical stack. Responsibilities Design and build core search engine components, including indexing pipelines, retrieval algorithms, and ranking systems that operate at the scale of billions of pages Develop streaming and batch data processing systems for search index construction in a high-load environment Push the limits of hardware performance through low-level optimizations and systems-level tuning Tackle challenges in multithreading, concurrency, and system-level optimization Qualifications 3+ years of hands-on experience in systems programming (Rust, C++, C, or similar) Ownership of full project lifecycle — you don't just write a fast inner loop, you care about how the system is built, deployed, operated, and scaled in production Knowledge of Python or other scripting languages Passion for writing clean, efficient, and scalable systems-level code Strong knowledge of algorithms and data structures, and the ability to apply them effectively Deep understanding of multithreading, including various approaches, challenges, and trade-offs Experience building high-load, distributed, and hardware-adjacent services Solid understanding of Linux internals (syscalls, networking stack, memory model, kernel tuning) Familiarity with low-level optimization techniques (memory management, cache efficiency, SIMD, profiling) Preferred Qualifications Experience developing core components of search engines, databases, or information retrieval systems Understanding of search fundamentals: indexing, query parsing, ranking, and relevance Experience with trading systems or other latency-sensitive real-time systems Familiarity with cloud services, Kubernetes, and AWS infrastructure