Sourcer Jobs

... Address: 10th Floor, Building 9, Tower B, DLF City, Gurgaon, Gurgaon, Haryana, 122002 Facility: MA-IN-GRN-CONTROLLABLE Qualification: Not Specified Experience: 1 - 2 years Source: Sodexo India | Job Code: IJP567050

... Address: 1904 , 19th Floor , Parine Crecenzo Bldg, Near MCA Club , G Block , BKC , Mumbai - 400051 Facility: BHARTI AXA LIFE INSURANCE - FM MUMBAI Qualification: 8th Experience: 1 - 2 years Source: Sodexo India | Job Code: IJP567047

... Address: 1904 , 19th Floor , Parine Crecenzo Bldg, Near MCA Club , G Block , BKC , Mumbai - 400051 Facility: BHARTI AXA LIFE INSURANCE - FM MUMBAI Qualification: Diploma Experience: 3 - 4 years Source: Sodexo India | Job Code: IJP567039

... Address: C/O Dr. Reddy\'s Lab - CTO Unit 1 Plot No: 127, 138, 145 & 146 SV Cooperative Industrial Area, Bollaram, Jinnaram, Medak Dist - 502 325. TelanganaHyderabadTSINDIA500034 Facility: DR. REDDY'S LAB - CTO UNIT 1 Qualification: ITI Experience: 2 - 3 years Source: Sodexo India | Job Code: IJP567080

At Bolna, we’re building tools that change the way teams leverage Voice AI. We’re looking for a Founding Machine Learning Engineer to own the end-to-end lifecycle of building, evaluating, deploying, and improving models that power millions of production conversations. This is a high-impact, high ownership role where you won’t just work on Bolna’s ML stack—you’ll help build the foundation it scales on. Our team includes IIT alumni with experience at Bain, Atlassian, Uber, Zomato, and LinkedIn, and is backed by leading investors. Responsibilities: Build the data engine - Design pipelines to source and clean conversational voice data across Indian languages, accents, and telephony conditions. Fine-tune models that ship - Fine tune and train models to improve accuracy, speed, and reliability across different use-cases. Define what "good" means - Build evaluation datasets and benchmarks for transcription accuracy, voice naturalness, interruption handling, latency, and end-to-end conversation quality. Set up human-in-the-loop pipelines to capture subjective quality at scale. Ship to production - Work with the engineering team to deploy models into a latency-sensitive, high-volume system. Monitor performance in the wild, debug regressions, and iterate fast. Required Skills: 3+ years of hands-on ML experience with deep practical real-world experience in training models. Strong Python and PyTorch fundamentals with exposure in distributed training, and modern fine-tuning techniques (LoRA, QLoRA, DPO, RLHF, etc.). Training data as a first-class problem. Experience designing data pipelines from collection, cleaning, labeling, deduplication, augmentation and treating data quality as a core engineering discipline. Rigorous about evaluation. You know that "looks good in a demo" is not a benchmark. You build the evals before you trust the model. Speech model experience is a plus with real-time / streaming inference experience where you would have contributed to latency optimization

About the Team OpenAI Finance ensures the organization is positioned for long-term success as we pursue our mission. The Revenue team plays a critical role in enabling OpenAI to scale its commercial offerings—overseeing billing operations, deal desk, revenue systems, and revenue accounting. We work cross-functionally with Technical Revenue, Finance Data, and Revenue Systems teams to support complex commercial arrangements, improve operational efficiency, and maintain financial integrity. About the Role As a Revenue Accounting Manager, you will own revenue accounting processes for consumption and usage-based revenue recognition while helping implement and maintain the systems, data flows, and accounting rules that support accurate financial reporting. You will serve as a key execution partner on onboarding new revenue streams, Fusion Accounting Hub rule updates, translating accounting requirements into expected journal entries, source-to-general-ledger mappings, user acceptance testing, data validation, and controlled operating processes. We’re looking for a hands-on revenue accounting owner who combines strong close discipline with systems and data fluency, independently coordinates cross-functional implementation work, and strengthens the accounting infrastructure supporting OpenAI’s growth. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own the monthly close for usage-based revenue and payment processor accounting, including journal entries, reconciliations, variance analysis, controls, and supporting documentation. Prepare and review revenue-related journal entries while understanding the underlying transaction lifecycle, accounting methodology, billing arrangements, source data, and expected financial reporting outcomes. Perform reconciliations for key revenue accounts, investigate discrepancies, and drive issues to resolution. Own flux

About the Team OpenAI Finance helps ensure the organization is positioned for long-term success as we pursue our mission. The Strategic Sourcing & Procurement team enables OpenAI to scale responsibly, securely, and at speed by helping teams choose the right external partners, structure strong commercial agreements, and build resilient supplier ecosystems. We work at the intersection of innovation and execution, partnering closely with leaders across the company to turn rapidly evolving needs into scalable, compliant, and economically sound solutions. Professional services are a critical source of specialized expertise, capacity, and operational leverage across OpenAI. Our work spans consulting and advisory services, finance and accounting, legal, people and talent, managed services, and other enterprise capabilities. Done well, sourcing becomes a source of trust and momentum—helping teams move faster with the right partners, clearer outcomes, stronger economics, and appropriate safeguards. About the Role We are seeking a Strategic Sourcing Lead to own and execute OpenAI’s category strategy for Professional Services. This is an experienced individual-contributor role for a high-velocity, hands-on sourcing operator who can set category priorities, own complex work end to end, exercise sound judgment, influence senior stakeholders, and build scalable category mechanisms in a rapidly growing organization. You will turn incomplete information, shifting priorities, and unclear decision paths into practical next steps and disciplined execution. You will manage a broad portfolio of services engagements—from strategic advisory relationships and enterprise programs to high-volume statements of work. Partnering directly with leaders across Finance, Accounting, Legal, People, Extended Workforce, and business teams, you will set category priorities and translate needs into clear sourcing strategies, executable engagement models, and measurable outcomes. You will operate inde

About the Team OpenAI’s People Experience & Technology (PXT) team owns the core people platform that powers worker, recruiting, contingent, approvals, and lifecycle workflows across the company. PXT is responsible for operating Workday, Ashby, and related people systems as governed, reliable sources of truth, while building the controls, monitoring, documentation, and auditability required to support scale. About the Role We’re hiring an Enterprise Systems Manager, Recruiting Systems to help own and harden OpenAI’s recruiting platform, with a focus on Ashby and its connected workflows. This is a hands-on systems role for someone who can translate recruiting process problems into governed, durable fixes through configuration, workflow design, access controls, documentation, reporting guardrails, and integration partnership. You will work at the boundary of Recruiting, HR Operations, Legal, Compensation, Analytics, IT, and PXT to improve the reliability and control health of recruiting workflows. The right person is comfortable going deep in system design while also driving rollout, adoption, and operational clarity. In this role you will: Own specific recruiting workflow domains in Ashby and adjacent tools, including stages, fields, permissions, approvals, templates, and configuration standards. Partner on high-priority remediation work across start dates, offers, approvals, integrations, auditability, data integrity, and workflow controls. Design and implement governed workflow changes that balance recruiter usability with reporting trust, downstream integration reliability, and control requirements. Establish and maintain guardrails such as required and conditional fields, stage definitions, role-based permissions, approval logic, validation patterns, and change standards. Drive durable fixes for recurring operational issues by identifying root causes and resolving them through configuration, automation, documentation, or process redesign. Partner with PXT, IT,

About the Team The Coding team is reimagining how software is built in the AI era. We build tools and workflows that help software engineers work faster, tackle more ambitious projects, and spend less time on repetitive tasks. AI has already transformed how code is written, but software engineering extends far beyond coding. Our mission is to apply AI across the entire software development lifecycle (SDLC) — from design and implementation to code review, testing, debugging, issue remediation, maintenance, documentation, and user support. The team is also responsible for developer-facing Codex experiences including the Codex IDE Extension and the terminal interface, which are used daily by developers ranging from individual open-source contributors to some of the world’s largest engineering organizations. The team also works closely with the open-source software community, building tools that help maintainers and contributors manage increasingly complex projects. We believe AI can make open-source development more sustainable by reducing the operational burden of reviewing contributions, triaging issues, maintaining quality, and supporting growing communities. By building the future of software development, we're helping advance OpenAI's mission of ensuring that the benefits of AI reach people around the world. About the Role We’re hiring a Full Stack Software Engineer to help invent the next generation of AI-powered software development workflows. “Full stack” in this role means much more than traditional frontend and backend development. You'll own complete product experiences, spanning user interfaces, workflow orchestration, agent and prompt design, backend systems, and cloud infrastructure. This is a highly product-oriented role. You'll work directly on the workflows developers use every day, identifying bottlenecks and rethinking how software gets built in a world where AI agents are active participants in the development process. The features you ship will inf

Overview: The Data Acquisition team within the Foundations organization at OpenAI is responsible for all aspects of data collection to support our model training operations. Our team manages web crawling and GPTBot services and works closely with Data Processing, Architecture, and Scaling teams. We are looking for a skilled Full-Stack Engineer to join our Data Acquisition team to build and optimize the interfaces and tools that power our data infrastructure. Responsibilities: Develop and maintain full-stack applications that support data acquisition, including internal tools and dashboards. Collaborate closely with cross-functional teams, including Data Processing, Architecture, and Scaling, to ensure seamless data ingestion and workflow management. Design and implement APIs to facilitate data interactions between internal services and external data sources. Enhance user experience by developing intuitive web-based interfaces for managing and monitoring data pipelines. Optimize backend services for performance, scalability, and security in a distributed computing environment. Work with legal and compliance teams to ensure our data acquisition processes adhere to privacy regulations and best practices. Deploy and maintain infrastructure using Kubernetes and Infrastructure-as-Code (IaC) methodologies. Analyze system performance, conduct experiments, and improve data workflows to maximize efficiency. Qualifications: BS/MS/PhD in Computer Science or a related field. 4+ years of industry experience in full-stack development. Proficiency in frontend frameworks (React, Vue, or similar) and backend technologies such as Python, Node.js, or Go. Strong expertise in RESTful APIs, GraphQL, and database design (SQL and NoSQL). Experience building data-intensive applications that handle large-scale datasets. Familiarity with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker). Prior experience with web crawling and large-scale data processing is a

AI Systems Engineer - Codex Core Agents About The Team The Codex Core Agents team builds the agent harness that turns model capability into real-world action. We own the systems around the model: prompting and interpreting model outputs, executing actions safely in real environments, and feeding production experience back into better models and better agent behavior. This team sits close to research and works across the stack: harness, model interaction, inference, sandboxed execution, orchestration, evals, production reliability, and the performance envelope around tokens, latency, cost, capacity, and quality. The harness is open source and increasingly part of how models are trained and evaluated, making this one of the highest-leverage layers in Codex. About The Role We’re looking for engineers to build the AI systems that make Codex agents dependable in production. The ideal candidate is an agent-systems builder: hands-on across low-level systems and ML workflows, able to debug Codex behavior end to end across the harness, model behavior, inference/runtime stack, GPU fleet, and product surface. You’ll work with research, infrastructure, and product to design agent harness capabilities, run experiments and ablations across the model + system prompt + harness stack, build frameworks for assessing production agent performance, and turn messy failures into durable improvements. What You’ll Do Design and build the core agent harness and execution loop that lets Codex agents interpret model outputs, use tools, execute code, and complete long-horizon tasks safely. Build sandboxing, isolation, orchestration, state, and workflow infrastructure for agents operating in real development environments. Develop evaluation, experimentation, and debugging systems that distinguish harness issues, model behavior, inference/runtime issues, and product failures. Run ablations across prompts, model-facing interfaces, context construction, tool-use strategies, and harness behavior to

About the Team The IT and Security organization builds the systems, data foundations, and automation that help OpenAI operate securely and reliably at scale. We support critical domains across identity, access, infrastructure security, enterprise systems, and internal productivity. As OpenAI grows, audit readiness and control assurance increasingly depend on reliable data: accurate system inventories, access populations, change records, configuration state, exception signals, and evidence generated directly from source systems. Our goal is to move beyond manual evidence collection and build scalable data products, automated validation, and continuous control monitoring that make security and IT controls measurable, repeatable, and defensible. About the Role We are looking for an IT Controls Data Engineer to build the data infrastructure that powers audit readiness, IT controls, evidence automation, and continuous control monitoring. In this role, you will design and maintain the pipelines, datasets, models, validation logic, dashboards, and evidence exports that make IT controls measurable, repeatable, and defensible. You will work across Security, IT, Infrastructure, Engineering, Finance Risk Management, and auditors to turn complex system behavior into reliable control data products. This is a technical builder role. The ideal candidate is strong in data engineering and analytics engineering, comfortable working with enterprise and security system data, and able to explain data lineage, source-system behavior, and control logic clearly to technical and audit stakeholders. You’ll be responsible for Building reliable data pipelines, models, and datasets for IT controls, including access, identity, configuration, change, ticketing, exception, and evidence data. Creating data quality, lineage, reconciliation, and completeness checks that make control data defensible for SOX and other audit use cases. Designing automated evidence generation workflows that produce compl

About the Team OpenAI’s Financial Engineering (FinEng) team powers how revenue flows through our products—pricing & packaging, checkout, payments, subscriptions, and the financial infrastructure behind them. We partner with Product, Engineering, Risk, Finance, and Go-to-Market to make paying for OpenAI products seamless, reliable, and efficient worldwide. About the Role As a Data Scientist on FinEng, you’ll own the analytics and experimentation that improve our checkout and payments , subscriptions , and pricing & monetization systems. You’ll define the metrics that matter, build the source-of-truth data assets, and design experiments that increase conversion, reduce churn and payment failures, and expand global payment method coverage. Your work will directly influence revenue, customer experience, and how we scale internationally. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will Own checkout & payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency. Build and run the experimentation program for in-house checkout—define success metrics and guardrails, execute staged rollouts, and use offline incrementality when online tests aren’t feasible. Create operational visibility and source-of-truth data with FinEng Data Engineering—land team-level metrics, SLAs, and self-serve dashboards that drive proactive action. Lead subscription, retention, and monetization analytics—ship launch-readiness for new subscription features, reduce involuntary churn (e.g., targeted retrials/nudges), and develop elasticity/FX frameworks toward pricing optimality. You might thrive in this role if you have 5+ years in a quantitative role (data science, product analytics, or experimentation) in high-growth or fintech environments Fluency in SQL and Python ,

Job Description: Data Scientist, B2B Demand Generation, Growth & Measurement About the Role We are hiring a Data Scientist to lead measurement, experimentation, and decision science for B2B marketing demand generation. You will help us understand which marketing investments create incremental demand, qualified pipeline, and revenue and how to scale them efficiently. Our mandate is to build a rigorous, full-funnel view of how B2B marketing creates demand and moves prospects from awareness and engagement to qualified opportunities, closed-won revenue, and expansion. You will shape how we measure marketing impact and influence across channels, campaigns, audiences, and account segments. In this role, you will partner closely with B2B Marketing, Demand Generation, Growth, Sales, RevOps, Finance to connect marketing activity to qualified pipeline, customer acquisition, and efficient revenue growth. What You’ll Do Define north-star, leading, and guardrail metrics for B2B demand generation, including account engagement, qualified leads and opportunities, sourced and influenced pipeline, conversion rates, pipeline velocity, and incremental ARR. Design and execute measurement and experimentation strategies across channels and campaigns, using randomized tests, audience or geographic holdouts, lift studies, quasi-experimental methods, and other causal approaches suited to long B2B sales cycles. Analyze channel, audience, campaign, creative, content, landing-page, and account-segment performance to identify the drivers of qualified demand, funnel conversion, pipeline quality, and incremental revenue. Partner with Marketing, Sales, RevOps, Finance, Product, and Engineering to improve instrumentation, campaign taxonomy, CRM data quality, lead-to-account matching, and the operating cadence for acting on measurement insights. Build AI-native measurement and decision-support workflows, using LLMs and agents to synthesize campaign performance, surface growth opportunities, and h

About the Team The Safety Systems team is dedicated to ensuring the safety, robustness, and reliability of AI models and their deployment in the real world. Building on the many years of our practical alignment work and applied safety efforts, Safety Systems addresses emerging safety issues and develops new fundamental solutions to enable the safe deployment of our most advanced models and future AGI, to make AI that is beneficial and trustworthy. Learn more about OpenAI’s approach to safety About the Role As an Analytics Engineer in Safety Systems, you will play a pivotal role in building a data-centric culture, enhancing decision-making processes, and driving strategic initiatives through analytics. You will partner closely with Engineering, Research, and Data Science to develop and maintain canonical data sources and source-of-truth dashboards that enable both people and AI agents across the organization to derive trustworthy, actionable insights. You will own the consumption layer for safety metrics: defining intuitive, reliable ways for stakeholders across Safety Systems, partner teams, and leadership to understand the safety of our products, answer safety-related questions independently, and inform product decisions and company strategy. Most importantly, you will be a core member of the Safety Systems team, collaborating with researchers and engineers to advance our goals of safe, robust, and reliable AI. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Design and maintain canonical datasets that serve as sources of truth for safety metrics. Develop and refine data products such as dashboards, reports, agent-enabled workflows, and machine-readable interfaces that empower stakeholders to extract and analyze data independently. Work closely with stakeholders in Engineering, Research, and Data Science to understand their decision-making n