Python Developer Jobs

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’s User Operations team shepherds our customers’ adoption of AI and ensures that our customers' product experience is nothing short of exceptional. We are building the very first post-AGI support team. We resolve complex issues, provide technical guidance, and support customers in maximizing value and adoption from deploying our products. We work closely with Sales, Technical Success, Product, Engineering and others, to deliver the best possible experience to our customers at scale. OpenAI's customers represent a range of diverse backgrounds and maturity, from early-stage startups to established global enterprises. About the Role We’re looking for dedicated, experienced, and deeply curious individuals to help solve some of the most complex challenges faced by our customers while building the future of post-AGI support alongside us. In this role, you’ll work directly with customers through support tickets and live calls, troubleshooting high-impact issues and resolving novel, often ambiguous technical problems in one of the fastest-moving environments in technology. As AI adoption rapidly accelerates, the work you do will directly support mission-critical systems being built on OpenAI’s platform, serving as a critical line of defense for customers operating at massive scale. Beyond resolving technical issues, you’ll help define what world-class support looks like in an AGI-driven future. You’ll partner closely with Engineering, Product, and Operations to improve systems, reduce bugs, and elevate the customer experience, while leveraging automation, agents, and our own AI technology to transform how support operates at scale. This Toronto-based role is currently remote and is expected to transition to an in-office arrangement. In this role, you will: Work directly with customers to troubleshoot and resolve their most complex technical issues, including API failures, integration challenges, authentication errors, and production incidents. Providing

About the Team OpenAI’s Network Engineering team within IT and Security advances the mission of deploying artificial general intelligence (AGI) for the benefit of all by delivering secure, scalable, and resilient network services. We build and operate the connectivity that supports OpenAI’s offices, labs, campuses, cloud environments, people, and devices. By combining strong network fundamentals with security, reliability, automation, and user-centered design, we enable impactful AI research, corporate operations, and product innovation. About the Role As a Network Engineer at OpenAI, you will design, operate, and continuously improve the global networks that connect our offices, labs, campuses, PoPs, cloud environments, people, and devices. The role spans strategic platform engineering and responsive production operations: you will shape architecture, standards, roadmaps, lifecycle plans, and automation while supporting incidents, escalations, and time-sensitive delivery. Operational signals will inform what we stabilize, simplify, standardize, or automate next. We work backward from user needs, investigate root causes, own outcomes end-to-end, and move quickly without compromising security. We are looking for a versatile engineer who can make pragmatic reliability and security tradeoffs, communicate clearly, and turn recurring operational work into durable platforms, tooling, and standards. You will partner across IT, Security, AppEng, Research, Applied, workplace teams, carriers, and vendors. In this role, you will: Design, implement, and operate secure, scalable enterprise networks across offices, labs, campuses, PoPs, cloud connectivity, and hybrid environments. Set strategic direction for network services through architecture, standards, roadmaps, lifecycle planning, capacity strategy, and measurable reliability outcomes. Own production operations, including on-call, incident response, escalations, and time-sensitive delivery, while protecting user experience,

About the Team The GTM Intelligence Solutions team builds the data and decision systems that help customer-facing teams take the right action at the right time. We combine product telemetry, commercial data, customer context, and field activity to identify account health and opportunity, recommend actions and use cases, deliver intelligence through field-facing products and agent workflows, and measure what happens next. We’re looking for a Data Scientist to help build the next generation of GTM intelligence at OpenAI. You will own a flexible portfolio of high-impact decision data products and work closely with Technical Success and other GTM teams to ensure the work drives better decisions. About the Role As a Data Scientist on GTM Intelligence Solutions, you will define and build the intelligence systems that help customer-facing teams prioritize accounts, identify risks and opportunities, choose interventions, and understand what worked. You will set the roadmap and methodology, build canonical features, ship reliable production workflows, monitor quality and adoption, and improve the systems using field feedback and business outcomes. This role combines hands-on technical depth with strong product and business judgment. You should be as comfortable writing production Python and advanced SQL, defining durable data contracts, and operating decision products as you are evaluating a ranking approach or designing an experiment. You will personally ship reliable first versions and partner with Analytics Engineering and Data Engineering when work requires shared infrastructure or additional scale. In This Role, You Will Set the roadmap and methodology for GTM intelligence and decision products, using deep stakeholder discovery to probe beyond stated requests, uncover the underlying decisions, workflows, constraints, and measures of success, and translate them into measurable systems. Own the full lifecycle of intelligence products, including feature definition, methodo

About the Team OpenAI's Industrial Compute organization is building the world's most advanced AI infrastructure ecosystem. Through a combination of strategic partnerships and self-built campuses, we are scaling the compute, storage, and networking platforms that power frontier AI training and inference. The Scaling Analytics team builds the data and software systems that help Industrial Compute understand, plan, and operate infrastructure at global scale. We work across capacity, hardware, storage, infrastructure software, and operational systems to connect fragmented sources of infrastructure data and make that information reliable and usable for engineering and planning. As OpenAI's infrastructure footprint grows, CPU and storage data increasingly spans internal platforms, vendor systems, APIs, databases, object storage, capacity management systems, and operational tooling. Building reliable connections across these environments is critical to understanding available capacity, utilization, fleet state, and infrastructure growth. About the Role We are seeking a Data Engineer to build the data systems and integrations that connect OpenAI's CPU, storage, and supporting infrastructure platforms. This role sits at the intersection of data engineering and backend software engineering. Rather than focusing primarily on traditional analytical pipelines, you will build the software and integrations required to collect, normalize, and make infrastructure data available across a heterogeneous set of systems. CPU and storage data may originate from internal infrastructure platforms, vendor APIs, databases, object storage, capacity systems, and operational services. You will determine how to reliably connect these systems and where those integrations should live—whether within an existing infrastructure service, an orchestration framework, a scheduled workload, or a purpose-built application. You will work closely with Infrastructure Engineering, Capacity Engineering, Storage,

About the Team GTM Growth Engineering builds AI-native products that help OpenAI's go-to-market and B2B marketing organizations scale with greater speed, intelligence, and operational effectiveness. We apply OpenAI models to real business workflows and build the systems that make those applications useful and dependable: customer context, agent behavior, feedback, evaluation, experimentation, and appropriate human oversight. Our work brings together software engineering, applied AI, product, data, and GTM operations. We measure success through the quality of customer engagement, pipeline, conversion, and the effectiveness of our sales and marketing teams. About the Role We're looking for an Applied AI Engineer to build production systems that help AI-powered go-to-market workflows improve over time. You will connect agent behavior, customer and operator feedback, evaluation, experimentation, and business outcomes to make these systems more effective, reliable, and responsive to evolving customer needs. This is a deeply technical, cross-functional role with end-to-end ownership of the agent improvement loop: understand production behavior, identify failure modes, improve how the system decides or acts, and validate the resulting impact. You will partner with Engineering, Product, Data Science, Sales, and B2B Marketing to turn real-world signals into safer, more effective agent behavior and measurable improvements in customer engagement, conversion, qualified pipeline, and team productivity. In this role, you will: Own the production improvement loop across agent behavior, customer and operator feedback, evaluation, experimentation, and verified business outcomes. Instrument agent workflows so model interactions, tool use, decisions, failures, human edits, and downstream outcomes can be understood in context. Define meaningful quality standards, representative evaluation datasets, regression coverage, and production monitoring for real GTM workflows. Investigate why a

About the Team Our economics team is continuously working to improve our understanding of an AI-driven economy. About the Role We are seeking a highly technical Economist to join the OpenAI Economic Research team studying the real-world economic impacts of AI. This role is designed for economists with up to 5 years of professional experience post-Ph.D. who are interested in using novel, large-scale datasets to study how AI is reshaping economic systems. We are looking for candidates with deep expertise in at least one core domain relevant to AI’s economic impact, and an interest in contributing to a broader research agenda spanning labor markets, firm behavior, market dynamics, and macroeconomic change. This is an individual contributor role where the candidate will organize and execute on their own data-oriented projects. You will work at the intersection of economic research, data science, and public policy to produce rigorous empirical work that informs decision-makers across the public, industry, and government. Research Areas of Interest We are particularly interested in candidates with demonstrated expertise in one or more of the following areas: Economic Measurement of AI Impact (e.g., adoption trajectories, labor market transitions, productivity growth, and forecasting/scenario modeling for AI-driven economic change) Macroeconomic Implications of AI (e.g., productivity, technology diffusion, economic growth) AI and the Labor Market (e.g., employment, wages, job search, task-level impacts, skill acquisition) Applicants are not expected to have experience across all domains. We aim to build a team with complementary strengths across these areas. In this role, you will: Design and execute empirical research using large-scale observational or experimental data. Apply causal inference and/or structural modeling techniques to study AI-driven economic change. Collaborate with cross-functional teams to translate research questions into testable frameworks and applic

About the Team OpenAI’s User Operations team shepherds our customers’ adoption of AI and ensures that our customers' product experience is nothing short of exceptional. We are building the very first post-AGI support team. We resolve complex issues, provide technical guidance, and support customers in maximizing value and adoption from deploying our products. We work closely with Sales, Technical Success, Product, Engineering and others, to deliver the best possible experience to our customers at scale. OpenAI's customers represent a range of diverse backgrounds and maturity, from early-stage startups to established global enterprises. About the Role We’re looking for dedicated, experienced, and deeply curious individuals to help solve some of the most complex challenges faced by our customers while building the future of post-AGI support alongside us. In this role, you’ll work directly with customers through support tickets and live calls, troubleshooting high-impact issues and resolving novel, often ambiguous technical problems in one of the fastest-moving environments in technology. As AI adoption rapidly accelerates, the work you do will directly support mission-critical systems being built on OpenAI’s platform, serving as a critical line of defense for customers operating at massive scale. Beyond resolving technical issues, you’ll help define what world-class support looks like in an AGI-driven future. You’ll partner closely with Engineering, Product, and Operations to improve systems, reduce bugs, and elevate the customer experience, while leveraging automation, agents, and our own AI technology to transform how support operates at scale. This role is based in Singapore. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. To succeed in this position you must be fully fluent in English (spoken and written). Please note that your resume must be submitted in English , and the interview process will be cond

About the Team OpenAI’s GTM Data Science team helps shape how our products are adopted, monetized, and scaled across organizations. We work at the intersection of Product, Go-to-Market, Finance, Research, and Data, turning product usage, customer evidence, and market signals into decisions that grow durable enterprise value. We’re looking for a senior Data Scientist to own the analytical strategy for enterprise knowledge-worker adoption. As ChatGPT Work and Codex become capable of research, analysis, document creation, spreadsheets, presentations, internal knowledge synthesis, and other agentic workflows, you will help determine how these products become embedded in everyday work—not merely tried once. About the Role You will define how we measure activation, retained usage, workflow depth, and value across ChatGPT Work, Codex, and connected enterprise systems. You will explain why adoption succeeds or stalls and identify the product, enablement, and commercial interventions most likely to create durable usage. This is a hands-on, zero-to-one role. You will work through imperfect telemetry, overlapping product surfaces, evolving definitions, and ambiguous business questions. You will partner closely with GTM, Product, Finance, Research, Customer Deployment, Analytics Engineering, and Data Science. Your work is successful when it changes a product, GTM, or investment decision. In This Role, You Will Define a trusted measurement framework for knowledge-worker adoption, including identity, eligible populations, activation, retained usage, penetration, workflow depth, feature adoption, and monetization. Map the knowledge-worker journey from initial exposure through first successful task, repeated workflows, multi-surface usage, and durable adoption. Identify which personas, functions, use cases, product capabilities, and account conditions are associated with deep and retained usage. Design and evaluate experiments and quasi-experiments across onboarding, enablement, wo

About the Team OpenAI’s Infrastructure Operations team is responsible for the availability, reliability, and operational excellence of one of the world’s largest AI infrastructure networks. The team owns day-to-day operations of production AI networks across Industrial Compute's data centers, working with colocation providers, deployment teams, and hardware vendors to deliver highly available GPU infrastructure for AI training and inference workloads. About the Role We are seeking an Infrastructure Operations Engineer to operate and improve the large-scale Ethernet fabrics that support GPU clusters, storage systems, and management infrastructure. This role combines hands-on production operations with automation, observability, and incident response across a global AI network. The ideal candidate has experience operating high-availability data center, cloud, AI, or HPC networks and can move comfortably from physical-layer troubleshooting to routing and fabric behavior, change execution, and root-cause analysis. You will partner closely with network architecture, systems engineering, GPU engineering, storage engineering, security, deployment, site operations, service providers, colocation partners, and hardware vendors to raise reliability and reduce operational toil. Key Responsibilities Own the operational health, availability, and reliability of production AI network infrastructure across Industrial Compute's data centers. Monitor, troubleshoot, and resolve network incidents while meeting service-level objectives (SLOs), reducing Mean Time to Detect (MTTD), and minimizing Mean Time to Recovery (MTTR). Operate and maintain large-scale Ethernet fabrics supporting GPU compute, storage, and management networks. Execute production network changes, maintenance windows, and capacity expansions with minimal customer impact. Manage the hardware lifecycle, including switch and optics replacements, RMA coordination, software upgrades, and preventive maintenance. Support new A

About the Team OpenAI’s User Operations team shepherds our customers’ adoption of AI and ensures that our customers' product experience is nothing short of exceptional. We are building the very first post-AGI support team. We resolve complex issues, provide technical guidance, and support customers in maximizing value and adoption from deploying our products. We work closely with Sales, Technical Success, Product, Engineering and others, to deliver the best possible experience to our customers at scale. OpenAI's customers represent a range of diverse backgrounds and maturity, from early-stage startups to established global enterprises. About the Role We are looking for an experienced, hands-on support leader to build and run the support and escalation motion for GPT-Rosalind and OpenAI’s life sciences customers. This is both a builder role and a frontline support role. You will work directly with customers, own complex cases, diagnose technical and operational issues, and coordinate cross-functional teams through resolution. You will also create the intake paths, playbooks, knowledge, tooling, and operating mechanisms needed to support life sciences workflows across ChatGPT, Codex, and the OpenAI API. You’ll partner closely with Account Directors, Product, Engineering and Forward Deployed Engineering, Security, Legal, and bioscience subject-matter experts. You will help customers receive coordinated, accurate answers while maintaining clear boundaries between technical support, scientific consultation, product feedback, and roadmap commitments. This role is an opportunity to define how OpenAI supports scientists and life sciences organizations as they adopt increasingly capable AI systems. In this role, you will: Work directly with Rosalind and life sciences customers, taking ownership of complex technical and operational support cases from intake through resolution. Serve as a critical escalation point and final line of defense for complex technical issues, partner

About the Team OpenAI's Human Data Team creates custom data solutions driving groundbreaking research. Our work enhances and evaluates our flagship models and products like ChatGPT, GPT-5, and Sora, and contributes to safety initiatives through collaboration with our Preparedness and Safety Systems teams. About the Role As a Research Program Manager (RPM) in the Human Data team you will partner with research and engineering to design and implement pragmatic solutions for collecting high-quality data. You will be a key interface between our research roadmap, external vendors, AI trainers, and the Human Data engineering team. This role is based in our San Francisco HQ. In this role, you will: Collaborate with Research: Partner with researchers to scope data collection needs, define success metrics, and establish quality measurement frameworks. Design & Execute Data Collection Campaigns: Translate research needs into actionable plans and accelerate execution by leveraging existing tooling and iterating to reach the desired outcome. In many cases, you will need to implement scrappy new solutions while partnering with engineering to design robust/scalable solutions. Unblock Yourself: You must be deeply uncomfortable with the idea of sitting around waiting for external dependencies, and have the technical acumen and drive to figure out how to achieve at least partial success in the interim. Optimize Systems & Processes: Build and optimize dashboards to track campaign performance, leveraging SQL and Python for data analysis and actionable insights. Drive Technical Roadmaps: Collaborate with engineers to enhance data platforms, resolve blockers, and ensure security best practices such as access management. Scale Your Impact : Advise and empower program managers and vendors to drive day-to-day execution so that you can focus on addressing high priority opportunities. You might thrive in this role if you: Are proficient in SQL and Python for data analysis, including q

About the Team OpenAI’s Safety teams work to ensure our products are safe, trusted, and resilient as frontier AI systems scale globally. We tackle some of the company’s most important challenges across understanding and preventing misuse and misalignment, intercepting fraud and abuse, and protecting vulnerable users. We are hiring Data Scientists to help build the analytical foundations that allow OpenAI to deploy increasingly capable AI responsibly. We are hiring Data Scientists across several teams that contribute to safety in different ways, including: Safety Systems Integrity Product Policy This is a high-impact role operating at the intersection of product, safety, policy, and research. About the Role As a Data Scientist, Safety, you will help solve complex and ambiguous problems where rigorous analysis directly informs critical decisions. Depending on your background and team alignment, you may work on areas such as: Measure harmful or abusive behavior across OpenAI’s products Detect fraud, manipulation, coordinated misuse Evaluate and improve safety classifiers, rules systems, mitigation systems, and human review workflows Design experiments and causal analyses to understand product, policy, and mitigation impacts Build prevalence estimators, dashboards, monitoring systems, and executive decision frameworks Diagnose gaps in safety and integrity systems using behavioral and product data, and help quantify and navigate false positive / false negative tradeoffs Translate ambiguous safety risks into measurable problems and evidence-based recommendations Partner with Product, Engineering, Policy, Research, and Operations teams to improve safety outcomes Build zero-to-one analytical systems in rapidly evolving domains Ideal Candidate We’re looking for strong Data Scientists who thrive in ambiguous, high-leverage environments. You may be a fit if you have: Strong statistical reasoning and analytical judgment Experience with experimentation, causal inference, or obse

About the Team Business Systems / Enterprise Platform Technology builds the internal systems, data foundations, workflow infrastructure, and enterprise platforms that help OpenAI operate at scale. The EPT AI Pod builds AI-native internal apps, MCP connectors, multi-agent workflows, and reusable platform capabilities across Finance, People, and GTM. About the Role As an Enterprise Applied AI Engineer, you will build internal apps for enterprise operations and the shared platform components those apps run on. This includes MCP connectors, multi-agent orchestration, data architecture, evals, monitoring, auditability, and governance. We’re looking for a hands-on engineer who is strong in Python, system design, enterprise integrations, data architecture, and applied AI systems. You should be excited to turn ambiguous business workflows into reliable internal products and shared infrastructure. In this role, you will: • Build internal apps for enterprise operations across Finance, People, and GTM • Build MCP connectors and enterprise integrations with strong auth, permissions, idempotency, retries, and rate-limit handling • Design end-to-end multi-agent workflows with tool routing, human approvals, audit trails, and safe action boundaries • Design data architecture for operational AI systems, including ingestion, schemas, quality checks, lineage, and governance • Build evals, monitoring, metrics, and regression tests for agentic workflows • Create reusable infrastructure, patterns, and components that other enterprise teams can build on • Partner with system owners and business owners to turn messy enterprise workflows into reliable internal products You might thrive in this role if you: • Have strong Python engineering skills for backend services, MCP connectors, agent/tool workflows, eval harnesses, and data ingestion jobs • Have strong system design skills across shared infrastructure, app architecture, reliability, and scaling • Have experience building internal apps,

About the Team The Hardware Health and Observability team owns the end-to-end health lifecycle of OpenAI’s global compute fleet. Our mission is to maximize healthy, usable compute across accelerator vendors, generations, cloud providers, and regions through reliable health signals, automated remediation, and scalable operational tooling. We build the systems that observe, detect, remediate, and verify hardware issues across GPUs, CPUs, networking, and platform infrastructure, enabling frontier model training and inference workloads to run reliably at hyperscale. We are the last line of defense for the success of OAI’s production and research workloads. About the Role On the Hardware Health and Observability team, you’ll build critical infrastructure that keeps OpenAI’s largest compute clusters healthy and operational at scale. Even small numbers of unhealthy systems can impact large-scale training and inference workloads. This team focuses on minimizing downtime, improving fleet efficiency, and ensuring compute resources remain continuously available to researchers and product teams. Engineers on this team own problems end-to-end, from defining health signals and debugging failures to building automated remediation systems that operate across millions of GPUs globally. In this role, you will: Define and maintain health signals across GPUs, CPUs, networking, and platform infrastructure. Build and evolve health checks that detect, remediate, and verify failures at scale. Ensure critical health checks execute with minimal latency to maximize workload uptime. Investigate hardware failures and system-level issues across large-scale compute environments. Own node lifecycle workflows including drain, quarantine, repair, RMA, and return-to-service processes. Build automation and tooling that enables global cluster management with minimal manual intervention. Partner with workload, reliability, and provider teams to integrate health signals into training and inference system