Beyond Borders: How MongoDB’s Talent Sourcing Team Fuels Global Hiring

Sunny Kathuria
August 16, 2023
#Culture

Our Talent Sourcing team is an integral part of MongoDB’s Talent Acquisition organization. Hear from Sunny Kathuria, Senior Manager of Sourcing, to learn more about his growing team and how team members have access to learning and development opportunities that are transforming their careers.

About the Sourcing Team

The Talent Sourcing team is centralized in India with global support capabilities and is divided into specialized sourcing groups by business units: Sales, Engineering & Product, Customer Engineering, and Corporate & Marketing.

The team’s strategy enables sourcers to act as true business partners to recruiters and hiring managers. Overall, we work towards the goal of generating a steady candidate flow for current and future positions by identifying, engaging, and networking with relevant talent pools of candidates, ultimately reducing the time to and cost of hire.

Along with sourcing talent, we have a group of Talent Sourcers who support open positions in India and engage with candidates over the phone, email, and LinkedIn InMail to prequalify them for recruiters.

The Talent Sourcing team at MongoDB contributes to nearly 25% of the overall hiring that takes place globally. The success of the team can be attributed to each sourcers’ knowledge of the industry, their effective utilization of sourcing channels, and their strong collaboration with recruiters and hiring managers.

Photo of the MongoDB Talent Sourcing Team

Hear from some of our team members

Himanshu Kalra, Senior Sourcer - Customer Engineering

I feel incredibly fortunate to be part of MongoDB's Talent Sourcing team. Working across regions, collaborating with global stakeholders, and hiring for diverse roles have contributed to my growth and continuous learning. I started as a Sourcer for Customer Engineering in APAC and was promoted to Senior Sourcer covering North America and India. This expansion provided valuable insights into different markets and talent landscapes. MongoDB's exceptional work culture and values stand out in our Talent Sourcing team. We encourage idea sharing, seek support from colleagues, and have autonomy in implementing strategies. I'm proud of the milestones achieved and excited about the future.

Mahima Saxena, Sourcer - Engineering & Product

Since joining MongoDB as a Technical Sourcer, my journey has been exceptional. The culture of collaboration and continuous improvement is a standout aspect of working here. MongoDB provides the necessary tools, resources, and training for us to excel in our roles, along with ongoing professional development programs to enhance our sourcing skills and stay up-to-date with industry trends. I’ve also had the opportunity to attend an offsite where I met senior leadership and gained valuable insights into our future plans. These experiences have reinforced my trust in MongoDB for my career. I feel privileged to be part of an organization that values its employees, fosters professional and personal growth, and provides a platform for success.

Melwin Dsouza, Sourcer - Corporate & Marketing

What sets MongoDB apart is its unique work culture. The inclusive and supportive environment motivates me to bring my best to the table each day. I am grateful for the constant support provided by leadership, who have been instrumental in my growth and development. There is a consistent focus on recognising employees which further reinforces my sense of accomplishment and fuels my drive to excel. One aspect that truly stands out for me is MongoDB's remote working policy, which has been a boon for maintaining a healthy work-life balance.

Syed Binish Jawaid, Senior Sourcer - Sales

I’ve been captivated by MongoDB’s amazing culture and inclusive work environment that embraces diversity and inclusion. Ideas are encouraged, collaboration is valued, and collective achievements are celebrated, regardless of background or identity. I’m constantly amazed by the abundance of learning opportunities available here, which have allowed me to grow from a Sourcing Specialist to a Senior Sourcer. Shortly after joining, I had the privilege of being mentored by brilliant leaders and also became a mentor to new team members. This exposure not only facilitated my professional growth but also helped me become an expert in sourcing top sales talent across the globe. I’ve witnessed the genuine investment team members have in each other’s growth, which inspires and motivates me every day.

Ankur Sharma, Sourcer - Sales

Being part of the MongoDB Talent Sourcing team has been an incredibly exciting experience for me. I have the privilege of working alongside some of the brightest and most hardworking individuals with a diverse range of skills. One aspect that makes working at MongoDB truly unique is the opportunity to engage with various business units and global stakeholders, each with their own distinct cultures. This dynamic environment ensures that my learning curve is always steep and keeps me continuously challenged. I am proud to be a part of a company that fosters a top-notch culture where we not only work hard to deliver exceptional results, but we also celebrate our achievements and enjoy ourselves to the fullest.

Garima Fulara, Senior Sourcer - Corporate & Marketing

Since joining the team three years ago, each day has presented me with valuable learning opportunities and a platform for personal growth. Being a part of the Talent Sourcing team brings me immense pride, as my contributions are pivotal in driving the company’s growth by aligning the ideal professionals with the right positions. During my time at MongoDB, I’ve witnessed significant expansion in terms of employee numbers, new regions, business units, products, and more. Knowing that I’ve played a role in facilitating this growth fills me with a strong sense of accomplishment.

How to succeed on the team

We look for individuals with strong research skills, knowledge of LinkedIn Recruiter, and a drive to go above and beyond to find great candidates, especially for niche roles and geographies. If you are applying to be a tech sourcer, you need strong conceptual technical knowledge of topics such as MongoDB fundamentals, products, and competitors; databases; software development lifecycle (SDLC); web services and microservices; DevOps, DataOps, and TechOps; and distributed systems. Experience in sourcing from unconventional tools such as GitHub is highly desirable.

Our interview process involves a live sourcing test followed by interviews with the hiring manager, department head, and stakeholders. For sourcing tests, we ask candidates to read a job description, prepare a boolean based on their understanding of the job requirements, and then run a search. Candidates are assessed on their approach and engagement with the interviewer throughout the conversation, more than the number and quality of search results.

The rest of the interview rounds focus on culture, relationship-building skills, communication, and articulation. Overall, if you have attention to detail, great communication skills (sharing your observations and asking the right questions), are research-oriented and can be creative with boolean strings, we would love to network with you!

Interested in pursuing a career on the Talent Sourcing team at MongoDB? We have several open roles and would love for you to transform your career with us!

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Building AI with MongoDB: Announcing the First Qualifiers for the Innovators Program

Artificial Intelligence is igniting so many brilliant ideas for new products and services. But turning those ideas into reality is a path that even the brightest minds struggle to navigate without some help along the way. That’s why we launched the MongoDB AI Innovators Program back in June this year. Access to expert technical advice, free MongoDB Atlas credits, co-marketing opportunities, and – for eligible startups, introductions to potential venture investors – come together to help you “build the next big thing” in AI. Since opening the program, we’ve received applications from around the world addressing every industry and spanning the spectrum of generative to analytical AI use cases. From enterprise chat and video bots that improve customer service and unlock insights from vast internal information repositories, conversational intelligence for sales reps, AI agents for workflow orchestration, tools for talent recruitment and retention, identifying workplace burnout through to news classifiers and summarization, personal wellbeing assistants, and the generation of bedtime stories for children that make science and technology more accessible to them. We’ve been amazed by the breadth and pace of innovators building AI on top of MongoDB Atlas. In this blog post, I want to share an overview of three startups that have just qualified from our AI Innovators Program. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Elevating the edge experience: Deploy AI anywhere with Cloneable and MongoDB Cloneable provides the application layer that brings AI to any device at the edge of the network. The Cloneable platform empowers developers to craft dynamic applications using intuitive low/no-code tools, instantly deployable to a spectrum of devices - mobiles, IoT devices, robots, and beyond. By harnessing machine learning models, a business can seamlessly leverage complex technologies across its operations. Models are pushed down to the device where they are converted to a native embedded format such as CoreML. From here, they are executed by the device’s neural engine to provide low latency inference, computer vision, and augmented reality. Cloneable uses MongoDB Atlas Device Sync to persist data locally on the device and sync it to the Atlas database backend in the cloud. This unique synergy creates an ecosystem where enterprise apps become real-time gateways to track, measure, inspect, and respond to events across an operation. The company is also exploring creating vector embeddings from images and data collected on devices and storing them in Atlas Vector Search . With this expanded functionality, users can better search and analyze events collected from the field. Predicting risks to public safety ExTrac draws on thousands of data sources identified by domain experts, using AI-powered analytics to locate, track and forecast both digital and physical risks to public safety in real-time. Initially serving Western governments to predict risks of emerging or escalating conflicts overseas, ExTrac is expanding into enterprise use cases for reputational management, operational risk, and content moderation. “Data is at the core of what we do. Our domain experts find and curate relevant streams of data, and then we use AI to anonymize and make sense of it at scale”, said Matt King, CEO at ExTrac. “We take a base model, such as RoBERTa or an LLM, and fine-tune it with our own labeled data to create domain-specific models capable of identifying and classifying threats in real-time.” Asked about why ExTrac built on MongoDB Atlas, King said “The flexibility of the document data model allows us to land, index, and analyze data of any shape and structure – no matter how complex. This helps us unlock instant insights for our customers.” King went on to say “ Atlas Vector Search is also proving to be incredibly powerful across a range of tasks where we use the results of the search to augment our LLMs and reduce hallucinations. We can store vector embeddings right alongside the source data in a single system, enabling our developers to build new features way faster than if they had to bolt-on a standalone vector database - many of which limit the amount of data that can be returned if it has meta-data attached to it. We are also moving beyond text to vectorize images and videos from our archives dating back over a decade. Being able to query and analyze data in any modality will help us to better model trends, track evolving narratives, and predict risk for our customers.” Access to technical expertise provided by the AI Innovators Program will help ExTrac manage the ever-growing size of its data sets – keeping performance high and costs low as the business scales. Using AI to cut maritime emissions and risk CetoAI provides predictive analytics for the maritime industry; combining high-frequency data, engineering expertise, and artificial intelligence the company reduces machinery breakdowns, cuts carbon emissions, and manages operational risk. Sensors installed onto each vessel generate real-time data feeds of engine and vessel performance. The data is used by the company’s AI models for predictive maintenance, optimizing fuel consumption, and carbon intensity forecasting, with the outputs consumed by the vessel’s crew, owners, and insurers. The data feeds generated by CetoAI are highly complex. Sensors on each vessel emit around 90,000 JSON documents daily. Each document stores around 100 unique time-series measurements, all requiring heavy-duty analytics processing before feeding machine learning models. It was these demands that led CetoAI’s engineering team to select MongoDB, migrating from a standalone time-series database that couldn’t keep pace with business growth. Sensor measurements from each data feed are streamed through Microsoft Azure’s IoT hub and ingested into MongoDB Atlas’ purpose-built time-series database collections . Here MongoDB window functions process and transform the data before serving it to CetoAI’s machine-learning models built with PyTorch and Scikit-Learn. CetoAI is now exploring additional capabilities available with MongoDB to expand its offerings. Atlas Device Sync can persist data locally and sync it between vessels and the cloud, withstanding the loss of network connectivity. Atlas Vector Search can be used for Retrieval Augmented Generation with the company’s LLMs. These are being developed to help crews diagnose and remediate equipment failures using natural language queries. Access to Atlas credits and expert support provided as part of the AI Innovators program enable CetoAI to accelerate and derisk the delivery of these new services. Get started Today we’ve focused on just three startups – there are many more that are already enjoying the benefits of the AI Innovators Program . We have more places left, but they are filling up fast, so sign up directly on the program’s web page. Also, check out our MongoDB for Artificial Intelligence resources page for all of the latest best practices to get you started on building the “next big thing” with AI.

August 16, 2023

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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024