Research Engineer In Data Jobs

About the Team This team builds an AI-native predictive analytics platform that embeds ML and AI-driven insights directly into production Go-To-Market workflows — powering real-time decisions at scale. The team owns the full stack from distributed data pipelines and backend services to ML and AI-powered capabilities that ship directly to customers. We are building toward a model where AI components are first-class runtime dependencies, not bolt-on features. Agentic AI development is a core part of how we increase engineering velocity and deliver customer value. This is a team that ships daily, iterates constantly, and treats speed as a capability to be deliberately improved. The Role This is a high-ownership, builder-first Sr. Software Engineer role. You will design, build, and ship AI-integrated data systems from concept through production — owning outcomes end-to-end, including deployment, monitoring, cost, and business impact. We are seeking a candidate who views AI tooling as a fundamental force multiplier in their daily engineering process. This position is central to our transition into an AI-native function, requiring an individual capable of making decisive, pragmatic architectural choices on reversible matters to maintain momentum. We need an experienced builder of production-grade, data-centric systems who is obsessed with delivering customer value and possesses a deep, curious enthusiasm for the transformative potential of AI. What You Will Build AI-Native Systems Development. Design, build, and own scalable data and ML pipelines, backend services, and AI-powered capabilities that are part of the platform's production decision-making layer. AI and ML components are runtime dependencies in this role — not research projects or experiments. Candidates will have strong back end and data engineering skills to thrive in this space. Daily Shipping. Decompose complex work into safely mergeable increments and ship them daily. Treat large, multi-day pull requ

Software Engineer Build technology where every nanosecond matters. At Graviton, software isn't just a tool that supports trading. It is the infrastructure behind every research breakthrough, every trading decision and every competitive advantage. As a Software Engineer , you'll work on systems where performance, reliability and precision matter at an extraordinary scale. You'll partner closely with software engineers and quantitative researchers to build technology that processes enormous volumes of market data, powers quantitative research and supports live trading. You'll take on problems that don't have obvious answers — from designing high-performance systems and distributed infrastructure to eliminating bottlenecks measured in microseconds and building tools that make our researchers and engineers faster. Your work will go into production, be measured against real-world performance and have a direct impact on how our trading systems operate. If you enjoy solving hard engineering problems, understanding systems at a deep level and pushing technology to its limits, you'll feel right at home. What You'll Work On You'll work across the engineering stack that powers our quantitative research and trading platforms. Depending on your team, your work may include: Designing and building high-performance, low-latency systems in modern C++. Building distributed systems that process and analyze massive volumes of market data . Designing systems where latency, throughput and reliability directly influence trading performance . Working on Linux systems, networking, concurrency and multithreaded applications. Profiling systems, identifying bottlenecks and optimizing performance at the hardware and software level. Building robust infrastructure that supports quantitative research and live trading. Debugging complex production systems and solving problems where correctness and reliability are critical. Designing internal platforms and developer tools that accelerate research an

Role: Application Reliability Engineer Location: Gurgaon Who we are Graviton Research Capital is a privately funded quantitative trading firm striving for excellence in financial markets research. We trade across a multitude of asset classes and trading venues using a diverse range of concepts, from time series analysis and stochastic models to machine learning and statistical inference. We analyse terabytes of data to identify pricing anomalies and drive innovation in financial markets. Key Responsibilities and Deliverables The ideal candidate will possess a strong background in technical support, with a passion for problem-solving and a commitment to excellence. As an Application Reliability Engineer, you will be responsible for: Monitor production services and respond quickly to alerts, incidents, and outages to ensure smooth operation and minimal downtime. Monitor trading systems and infrastructure., Triage issues across trading support services, databases, and infra; escalate and coordinate with the right owners, and drive root-cause analysis and ensure fixes are implemented for long-term stability. Serve as the first line of defense for trading operations. Proactively identify, address recurring issues, and build automation to reduce manual intervention. Improve observability by enhancing monitoring, logging, and alerting systems. Develop and maintain operational runbooks and SLO/SLA metrics. Eligibility and Required Skills Possess a degree in a highly analytical field, such as Engineering, or Computer Science 2-5 years of experience in Python, Shell/Bash scripting. Experience with Linux and shell/bash online tools. Hands-on experience with databases (SQL, NoSQL) Strong problem-solving and analytical skills Excellent communication skills Ability to remain calm and analytical under production pressure Good to have: Familiarity with monitoring/alerting stacks (Prometheus, Grafana, ELK, etc.) Familiarity with distributed messaging (Kafka) and caching systems (Red

P-1408 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products . Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Some of them can have hundreds of millions of assets. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking a Staff Engineer to lead enhancements to our Search Quality. In 2025, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. 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,

P-1408 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products. Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Some of them can have hundreds of millions of assets. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking a Staff Engineer to lead enhancements to our Search Quality. In 2026, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. 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, w

P-926 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products. Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking multiple senior engineers to drive enhancements to our Search Quality. In 2026, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. 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. 5+ 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 red

P-375 The Search team at Databricks sits at the forefront of advancing AI/ML-powered products. Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking a Staff Engineer to lead enhancements to our Search Quality. We are focusing on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. 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 inte

Description: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets research. We are seeking a Software Engineer for our team in Gurgaon. Our Core Technology team has some of the best programmers in India working on cutting edge technologies to build a super fast and robust trading infrastructure handling millions of dollars worth of trading transactions every day. Requirements Contribute to all layers of backend systems including databases, APIs and applications Design, build and maintain applications for business requirements Architect scalable and reliable applications Write clean and modular code, following good coding standards and practices Troubleshoot and debug applications Be involved in the entire application lifecycle Collaborate with multidisciplinary team of front-end developers, engineers and system administrators Devise innovative solutions to address new and complicated challenges Build reusable code and libraries for future use Take lead on projects, as needed Work in a high paced competitive environment Qualifications The ideal candidate will have: Engineering degree in Computer Science (preferred) or any other discipline from a Tier 1 college. 3 to 4 years of relevant experience in designing and improving information systems Proficiency in Python programming Extensive experience working with relational databases and handling large datasets Good Understanding of object oriented and asynchronous programming Familiarity with front-end languages such as HTML, JavaScript and CSS Knowledge of Linux systems and bash scripting Good communication and interpersonal skills Willingness to continuously learn and improve Ability to work in fast paced environment under pressure and manage multiple high priority projects Good to have: Experience in Financial services space/domain Experience in and understanding of system design decisions Experience in leading small teams or projects Benefits: Our open and casua

RDQ127R59 Summary As a Senior Applied ML Engineer on the Applied AI team at Databricks, you will use machine learning, scheduling, and optimization algorithms to maximize the efficiency and performance of our infrastructure. Your work will span the entire stack—from cluster management down to query compilation. You will solve complex, high-impact engineering problems to deliver highly optimized, cost-effective workloads for our customers. Impact You Will Have Accelerate Serverless Growth: Drive the scaling and efficiency of Databricks serverless compute products through advanced optimization techniques. Build Systems: Design end-to-end ML4Sys solutions from the ground up within a lean team of domain experts to support Shape Strategy: Define the roadmap for applied ML investments by collaborating with engineering and product leaders across Databricks. Drive Deployment: Architect, train, and deploy state-of-the-art models that directly improve product performance and cost efficiency. Scale Infrastructure: Build robust ML pipelines, data processing layers, model serving components, and production monitoring systems to help scale Innovate: Research and implement novel modeling techniques tailored specifically to computer systems and distributed environments. Minimum Qualifications Education: Background in Computer Science and Master's degree in Machine Learning, Data Science, or a related computational field (AI, Bioinformatics, EE, Physics, etc). ML Experience: Strong background in building, training, and deploying machine learning models in production. Infrastructure Knowledge: Practical familiarity with cloud computing, distributed systems, and modern data processing frameworks. Core Coding: Proficiency in Python, Scala, or Java. Preferred Skills Advanced Education: PhD in AI, Data Science, or a related technical discipline. Industry Experience: 4+ years of machine learning engineering experience in high-velocity, high-growth environment. Systems Domain:

Role: Network Engineer Location: Gurgaon Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Network Engineer for our team in Gurgaon. Graviton trades across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition to statistical inference analysing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. Key Responsibilities Design, deploy, operate, and troubleshoot low-latency network infrastructure used by trading firms. Manage connectivity to global stock exchanges, brokers, market-data providers, and ISPs. Build and maintain colocation infrastructure including routers, switches, Layer-1 devices (added advantage), structured cabling and cross connects. Configure and support Cisco Nexus, Arista and similar platform devices. Design and troubleshoot Layer 2 and Layer 3 networks including: VLANs, VRFs, BGP, OSPF, Static routing, PIM, IGMP, Multicast, SSM, ACLs and QoS. Troubleshoot packet loss, multicast issues, duplicate packets, IGMP/PIM and multicast/BGP routing. Monitor and optimize latency, jitter, packet loss, interface errors, congestion, and network performance. Work with ultra-low-latency technologies including: Cut-through switching, Layer-1 switches, FPGA-based network devices, Kernel-bypass networking, ExaNIC/Solarflare NICs, Hardware timestamping. Configure and troubleshoot PTP and clock synchronization infrastructure. Perform server and network equipment installation in exchange and third-party data centres. Manage rack layout, patching, cable optimization, optics, DACs, cross-connects, and inventory. Coordinate network changes with exchanges, telecom providers, brokers, vendors, and data-centre teams. Plan and execute production changes during approved maintenance windows. Perform pre-change validation, c

About the Role We are looking for a skilled and detail-oriented Python Developer to join our team. In this role, you will focus on writing efficient scripts, analyzing complex data, and delivering actionable insights through clear and accurate visualizations. The ideal candidate will be a fast learner with a strong foundation in software development principles and a passion for solving challenging problems. Key Responsibilities Design and develop efficient Python scripts for data analysis and automation. Build accurate and insightful data visualizations to support decision-making. Implement and maintain alerting tools to monitor data workflows and systems. Collaborate with cross-functional teams to gather requirements and deliver solutions. Apply strong knowledge of data structures, algorithms, and OOP concepts to write performant, maintainable code. Continuously optimize and improve data processing pipelines for scalability and performance. Key Requirements Experience: 2–5 years as a Software Engineer or Python Developer. Strong proficiency in Python with the ability to quickly learn new frameworks and libraries. Hands-on experience with SQL for querying and data manipulation. Experience with data visualization/plotting tools (e.g., Matplotlib, Seaborn, Plotly). Familiarity with alerting and monitoring tools . Solid understanding of data structures, algorithms, and OOP concepts . Excellent problem-solving skills, analytical thinking, and attention to detail. Benefits: Our open and casual work culture gives you the space to innovate and deliver. Our cubicle free offices, disdain for bureaucracy and insistence to hire the very best creates a melting pot for great ideas and technology innovations. Everyone on the team is approachable, there is nothing better than working with friends! Our perks have you covered. Competitive compensation 6 weeks of paid vacation Monthly after work parties Catered breakfast and lunch Fully stocked kitchen International team outing

Role: VPN Engineer Location: Gurgaon Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Network Engineer for our team in Gurgaon. Graviton trades across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition to statistical inference analysing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. Responsibilities Manage and support corporate network infrastructure across multiple locations, including routers, firewalls, switches, wireless access points, VPN gateways, Internet links, and LAN/WAN connectivity. Configure and troubleshoot VPN technologies such as IPsec, SSL VPN, site-to-site VPN, remote-access VPN, WireGuard, OpenVPN, and FortiClient/FortiGate VPN, including issues related to authentication, tunnels, routing, DNS, packet loss, performance, split tunnelling, and firewall policies. Manage secure connectivity between offices, data centres, cloud environments, and remote users. Configure and maintain office LAN infrastructure, including VLANs, trunk/access ports, inter-VLAN routing, DHCP, DNS, NAT, ACLs, static routing, BGP, and OSPF where required. Manage multiple ISP connections, including primary and backup Internet links, automatic failover, and monitoring of utilization, latency, jitter, packet loss, and link availability. Coordinate with ISPs and telecom providers for new circuits, link failures, bandwidth upgrades, routing issues, packet-loss investigations, and service escalations. Manage firewall policies, NAT rules, VPN policies, network objects, and routing, while regularly reviewing and removing unnecessary access. Implement network segmentation across user, server, management, guest, and other business networks, while maintaining secure administrative access to network equip

P-1930 At Databricks, we run the world's largest AI platform. We believe the next breakthroughs will come from models trained on domain-specific enterprise data, not from bigger general-purpose models alone. That's why we built AI Runtime , a serverless product that makes GPU training of any size feel simple, so companies can train their own models the way frontier labs do. Customers across industries already use it to train models on governed data, from automotive AI at Rivian to Merck's TEDDY foundation models for single-cell biology and drug discovery, all the way to agents like KARL from Databricks AI Research. It takes a lot of engineering to make that work, and we've written about it on our blog: how we keep GPUs reliable , make training fault-tolerant , and speed up fine-tuning with fused kernels and smart sequence packing. As a Staff Software Engineer, you'll work on the systems behind all of this, solving hard technical problems and shaping major parts of AI Runtime. The impact you will have: Drive major parts of AI Runtime's architecture and help shape Databricks' long-term technical direction in AI training infrastructure. Design the reliability and observability systems for a fleet that keeps growing in scale and complexity. Lead improvements to AI Runtime's developer experience, so the largest training jobs feel as simple as a first experiment. Help choose the technical bets that prepare AI Runtime for new GPU generations and workload types. Take on the hardest multi-node training problems yourself, from orchestration and GPU scheduling to data loading and checkpointing. Make training faster and cheaper by raising GPU efficiency across model architectures and hardware generations. Work with research, product, and customers to shape what we build next and lead projects across teams. Champion engineering excellence, raise the bar, and mentor the engineers around you. What we look for: 10+ years in large-scale distributed systems, with significant depth in

P-1428 At Databricks, we run the world's largest AI platform. We believe the next breakthroughs will come from models trained on domain-specific enterprise data, not from bigger general-purpose models alone. That's why we built AI Runtime , a serverless product that makes GPU training of any size feel simple, so companies can train their own models the way frontier labs do. Customers across industries already use it to train models on governed data, from automotive AI at Rivian to Merck's TEDDY foundation models for single-cell biology and drug discovery, all the way to agents like KARL from Databricks AI Research. It takes a lot of engineering to make that work, and we've written about it on our blog: how we keep GPUs reliable , make training fault-tolerant , and speed up fine-tuning with fused kernels and smart sequence packing. As a Senior Software Engineer, you'll work on the systems behind all of this, solving hard technical problems and helping shape how AI Runtime evolves. The impact you will have: Build core parts of AI Runtime and contribute to Databricks' technical direction in AI training infrastructure. Develop reliability and observability features for a fleet that keeps growing in scale and complexity. Improve AI Runtime's developer experience, so the largest training jobs feel as simple as a first experiment. Help prepare AI Runtime for new GPU generations and workload types. Take on hard multi-node training problems, from orchestration and GPU scheduling to data loading and checkpointing. Make training faster and cheaper by raising GPU efficiency across model architectures and hardware generations. Work with research, product, and customers on what we build next and own features from design through production. Champion engineering excellence, raise the bar, and help the engineers around you grow. What we look for: 5+ years in large-scale distributed systems, with experience in GPU training, high-performance computing, or ML systems. Experience traini

P-78 At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems, from advancing AI research to powering next-generation applications. We do this by building and operating the world’s best data and AI infrastructure platform. Founded by engineers and driven by customer obsession, we embrace the hardest technical challenges, whether it’s scaling distributed systems across multiple clouds or delivering reliable, low-latency communication between thousands of services. And we’re only getting started. As a Senior Software Engineer on the Application Traffic team , you will design and build the systems that power Databricks’ service-to-service communication across thousands of clusters in a multi-cloud environment. You will also help create abstractions that hide networking complexity from product teams, making connectivity, discovery, and reliability seamless by default. The impact you’ll have: You’ll work across three key areas that define Databricks’ networking stack: Ingress Control Plane : Build the control plane for Databricks’ global ingress layer. Enable programming of API gateways with static and dynamic endpoints, simplify service onboarding, and make it easy to expose APIs securely across clouds. Service-to-Service Communication : Design scalable mechanisms for service discovery and load balancing across thousands of clusters. Provide networking abstractions so product teams don’t need to worry about underlying connectivity details. Overload Protection : Build intelligent rate limiting and admission control systems to protect critical services under high load. Ensure reliability and predictable performance for both customer-facing and internal workloads. What we look for: BS (or higher) in Computer Science or related field 5+ years of experience designing and building large-scale distributed systems Strong proficiency in one or more languages such as Java, Scala, Go, or C++ Experience with service-oriented architectures an