Measurement Lead Jobs

About the team Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. Mitigating the frontier risks resulting from these capabilities is paramount to OpenAI’s ability to continue deploying models safely. The Preparedness team is dedicated to addressing these critical risks. Our work includes: Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. Mitigation. Keeping misalignment safeguards, alignment tools, and on track to adequately address extreme threats that might arise in the future. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework , and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. About the role Preparedness is hiring strong technical executors to support preparations for accelerated AI development, which may culminate in recursive self-improvement. This work relies on anticipating misalignment risks that might exist in the future, but might not exist now; so it’s especially important that people in this role are tasteful and strategic. The role is wide-ranging, covering any mitigation for loss of control risk, spanning the design and implementation of better pre-deployment risk-assessment , control measures , RSI-relevant training interventions, and turning one’s technical work into established institutional practices and external-facing communications. Below is a subset of our focus areas: Scalable oversight: Establishing practices for model misbehavior monitoring and oversight which remain effective in superhuman model capability regimes, with a focus on bridging from today’s monitoring approaches to future-proof ones. Automated auditing: As model capabilities increase, we’ll increasingly rely on automated approaches for finding the most severe forms of model misalignments. We’ll both need to s

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evalua

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). - Experience with a deep learning fra

About Supabase Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. About the Role We're looking for a Senior Data Analyst, Marketing to join our Data Intelligence team and build the measurement foundation that tells us what's actually working across paid and PLG channels. You'll work closely with Marketing, Growth, and channel owners, helping us move past platform-reported metrics and vanity numbers into causal, trusted answers about what drives pipeline and revenue. This role is ideal for someone who thrives in async, fast-paced environments, is AI-forward in how they work, and is excited about building a measurement function from the ground up. What You'll Be Responsible for Marketing Measurement Strategy Own the end-to-end marketing measurement strategy across experimentation, media mix modeling, and attribution for paid and PLG channels Establish and evolve the attribution framework: how platform data, multi-touch attribution, MMM, and experiments work together to inform decisions Translate complex measurement outputs into clear recommendations on where to invest, what to cut, and how to hit pipeline, revenue, and efficiency targets (CAC, payback, LTV to CAC) Serve as the subject matter expert for marketing measurement, educating stakeholders on causality, model uncertainty, and the limitations of platform-reported metrics Incrementality and Experimentation Design and run always-on incrementality tests, user-level and geo-level, to quantify the causal impact of key channels, campaigns, and tactics Calculate incremental lift, incrementality percent, and incremental ROAS/CPA, and use these to guide budget reallocation Build repeatable analysis templates and playbooks for experiment design, analysis, and readouts so results are consistent across teams Partner

About the Team Enterprise Verticals builds role-specific ChatGPT Work experiences for high-value enterprise workflows. We combine product engineering, plugins and skills, connectors, data, evaluations, and customer evidence to turn useful demos into reliable daily work. This opening sits within the Technology vertical inside Enterprise Verticals. The group focuses on repeatable workflows for people at technology companies, beginning with functions such as data and analytics, sales, and design, and carries the shared platform needs—tool integration, permissions, quality measurement, and safe rollout—across those experiences. We work closely with Design, Research, GTM, Security, and platform teams, as well as with customers and design partners. Success means that people can reach a trustworthy first result, understand what the system did, and keep using the workflow—not merely that a prototype exists. About the Role We are looking for a full-stack product engineer who can own ambiguous enterprise workflows end to end: understand a customer problem, shape the product, build across frontend and backend, work through platform dependencies, instrument quality, and learn quickly with design partners. You will build across ChatGPT Work surfaces, services, plugins, connectors, and data or permission boundaries when the experience requires it. You will make quality and rollout observable through evaluations, product and operational signals, and clear fallback or rollback paths. This is a product-engineering role for someone who can move between user problems and system details without losing ownership of either. The strongest candidates will be able to turn specific customer evidence into a generalizable product, explain scope and architecture tradeoffs, and carry a feature from an early prototype through a bounded production rollout. In this role, you will: Build and ship role-specific workflows across ChatGPT Work surfaces, services, plugins, and connectors. Turn customer a

About Supabase Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. About this role We’re hiring experienced performance engineers to make performance measurement at Supabase rigorous, repeatable, and actionable by product teams. This role owns the benchmarking and profiling systems we use to detect regressions, explain performance changes, and publish credible numbers for customers and the market. You’ll build the tooling and the methodology and make it easy for product teams to use it correctly. You’d join a new performance team as one of its first members, working alongside a deeply seasoned performance engineer, with the team growing to roughly six this year. There’s no legacy process to inherit — you’ll help define how we measure and communicate performance metrics at Supabase. What you’ll do Build and evolve benchmarking, profiling, and load-testing tooling (cloud, database, and end-to-end). Define benchmark suites and reference workloads that reflect real customer behavior (and evolve them as the products change). Establish performance baselines and regression gates across products (variance tracking, environment control, and detection of changes). Build mechanisms that turn benchmark results into action: triage workflows, owner routing, and clear, reproducible reports. Partner closely with database teams (e.g. Multigres, OrioleDB) to identify bottlenecks and land concrete performance improvements. Enable the company to publish credible, repeatable performance numbers for customers and the market. Help teams self-serve performance testing and make performance a first-class engineering concern. Problems you might work on Build our continuous benchmarking infrastructure so product teams can spot performance regressions early. Ideally, teams can run benchma

About the Team GTM Growth Engineering builds AI-native products and systems that help OpenAI's go-to-market and B2B marketing organizations operate with greater speed, focus, and leverage. Our mandate is revenue leverage: products tied directly to pipeline quality, customer engagement, seller and marketer productivity, and the speed at which OpenAI can bring its technology to customers. We build the infrastructure and user experiences behind high-impact GTM workflows, including customer context, prioritization, routing, campaign execution, review surfaces, feedback loops, and measurement. Our work combines product craft, applied AI, reliable systems, and thoughtful operational design. About the Role We’re looking for a product-minded Software Engineer to build AI-powered products and full-stack experiences for GTM Growth Engineering. You will own meaningful product slices end to end, from user experience and frontend implementation to backend APIs, integrations, data models, instrumentation, and launch readiness. This is a role for engineers who want to build products that do real work in production. You will partner with Product, Design, Data Science, Sales, B2B Marketing, and operations teams to understand high-value workflows and ship systems that improve customer engagement, pipeline, conversion, and team productivity. The role is ideal for a strong product engineer who can move between product craft, systems engineering, applied AI, and measurable business outcomes. You should be excited to build from ambiguous problem statements, ship quickly, and improve products based on real user feedback. What You'll Do Build AI-powered products and workflows that help sales and B2B marketing teams identify opportunities, coordinate work, and engage customers more effectively. Own full-stack product experiences from prototype through launch, instrumentation, iteration, and production hardening. Design intuitive user journeys that combine polished interfaces, reliable servi

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. As the Senior, Internal Audit AI Enablement & Automation, you will own the day-to-day build, adoption, and measurement of Okta Internal Audit's AI automation program — turning auditor problem statements into working tools, tracking every measurable metric to a verified strategic outcome, and representing Internal Audit across Finance and company-wide AI networks. Okta Internal Audit has moved past the proof-of-concept stage. The AI enablement program is live, tools are in production, and meaningful audit capacity has already been recovered and redeployed into higher-value work — without external development spend. This role exists to scale that engine. The role is the hands-on builder, program coordinator, and adoption driver at the center of that effort. This is not a strategy or advisory role — it is a delivery role. The person in this seat ships tools, closes the loop on problem statements, translates recovered audit capacity into documented strategic value, and represents Internal Audit's automation program in the Finance AI Champions and Company-wide AI Champions networks. What You Will Own Automation Delivery - This is the core of the role. Everything else supports it. Build and maintain AI automations across all Internal Audit functions using the department's existing AI Hub and SOX testing engine as the foundation. Manage the problem statement intake pipeline, acknowledging every submission with a clear disposition (built, deferred, or declined

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

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role We're redefining how software is built and who gets to build it. Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI. Realizing that vision requires a platform that legitimate users can trust and adversarial actors cannot exploit. We're hiring a Data Scientist to help build Replit's Trust & Safety and Anti-Abuse program from the ground up. You'll turn noisy behavioral, identity, payment, infrastructure, and content signals into the measurement systems, detections, and decisions that protect Replit's users, platform, and economics. You'll work closely with Engineering, Support, Legal, Security, Infrastructure, Money, and Growth to make abuse economically unviable while keeping friction low for legitimate users. Replit sits at the frontier of AI-native abuse. Our platform is a target for phishing and scam hosting, cryptomining, LLM token farming, card and coupon fraud, referral abuse, and increasingly, abuse driven by AI agents themselves. You'll help define how we identify, measure, and respond to these threats without compromising the experience of good users. Who You Are You're a data scientist who moves fast, goes deep, and thinks adversarially. You can spin up an analysis in hours that would take others days, not by cutting corners, but because you've built the intuition and technical toolkit to get to the right answer quickly. You dig past the top-line abuse rate to understand selection effects, missing labels, policy changes, attacker adaptation, and the false positives hidden inside an aggregate metric. You understand that Trust & Safety data is imperfect and outcomes are high stakes. Ground truth is delayed, biased, and often incomple

We are looking for an Analytics and Tag Manager Implementation Specialist to join our digital marketing team. The ideal candidate will be passionate about web analytics, an expert in Google Tag Manager and possess strong Javascript skills. This professional will play a crucial role in improving our digital marketing strategies, ensuring the correct implementation of measurement and analytics tools. Responsibilities: 1. Implement and maintain data tracking solutions using Google Tag Manager to ensure accurate and complete data collection. 2. Collaborate closely with the marketing team to understand specific tracking objectives and requirements. 3. Develop and maintain JavaScript scripts to customize and enhance data collection. 4. Set up and manage Google Analytics 4 to analyze campaign performance and user behavior. 5. Perform regular audits to ensure data integrity and troubleshoot tracking issues. 6. Collaborate with other teams to integrate analytics data into marketing reports and dashboards. Requirements: 1. Demonstrated experience implementing and managing Google Tag Manager. 2. Advanced Javascript skills for tracking customization and troubleshooting. 3. Hands-on experience with Google Analytics 4, including event and goal configuration. 4. Deep understanding of digital marketing principles and the importance of analytics in digital strategies. 5. Strong analytical skills and ability to translate data into actionable recommendations. 6. Excellent communication skills and ability to work in a team environment.

About the Team The Core Models team helps shape how OpenAI’s frontier models are built, measured, and launched. We work across Research, Engineering, Model Design, Data Science, and Product to turn advances in model capabilities into reliable, useful experiences for people. Our scope includes model planning and launches as well as building data flywheels, evaluations and measurement systems to ensure our models have strong capabilities and behavior. About the Role As a Product Manager for the Core Models team, you'll be at the forefront of defining and guiding the future of how our AI models work in real-world applications. You will connect user needs to model and systems decisions: how prompts are understood; how information is aggregated and made useful for training and evaluation data; and how capabilities move from research prototypes into the mainline model and launch stack. You will operate comfortably across research, infrastructure, and consumer product surfaces, creating clarity where ownership and technical boundaries are still emerging. 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: Translate user and product goals into clear model requirements, system architecture choices, and research priorities across query understanding, indexing, retrieval, ranking, tool boundaries, data, training, inference, and evaluation. Build closed learning loops that turn product usage, explicit feedback, and other user signals into datasets, evaluations, experiments, training priorities, and launch decisions. Define success across offline evaluations and online product metrics, balancing model quality, usefulness, latency, safety, reliability, and cost. Partner closely with post-training research, applied product engineering, Model Design, and Data Science to integrate capabilities into the mainline model stack. Create reusable platforms and operatin

A Career with Point72's Regulatory Reporting Team Point72's Regulatory Reporting department's global mandate is to provide governance across all regulatory filings and obligations for the firm, including but not limited to ensuring completeness of the global inventory of filings, instituting process and workflow controls, facilitating the creation of policies and procedures, and providing process measurement reporting. The department is responsible for preparing reports, generating and coordinating responses for larger cross-departmental filings, managing external vendor relationships, oversight of Technology supporting regulatory filings, evaluating automation options, and administering data standards. The various regulators include but are not limited to the Securities and Exchange Commission (SEC), Commodities Futures Trading Commission (CFTC), National Futures Association (NFA), Federal Reserve/Treasury, Securities and Futures Commission (SFC), Kanto Finance Bureau (KFB), Financial Conduct Authority (FCA), and Monetary Authority of Singapore (MAS). What you'll do Support the preparation and review of filing responses for various regulators (e.g., SEC, NFA, FCA, MAS) Execute MiFID II transaction reporting obligations, including monitoring, validation, and remediation of reporting issues across relevant asset classes Gather requirements, perform functional and data analysis, and contribute to the development of functional and technical specifications for regulatory reporting processes Partner with business teams to clearly document requirements and workflows for regulations Contribute to the maintenance and improvement of the team's controls framework Conduct class action lawsuit analysis as required Respond to Compliance inquiries and regulatory data requests with timely, data-backed analysis Conduct position and transaction monitoring checks to ensure reporting accuracy Utilize data analytical and automation tools to detect anomalies in reporting data and improv

The Construction Planning Team Member supports project planning by developing preliminary L3/L4 EPC schedules and tracking project progress through S-Curves and other visual tools. They ensure timely updates by preparing and maintaining progress reports, including daily, weekly, and monthly reports, and by utilizing the Progress Measurement System (PMS). The role involves providing performance insights through Target vs. Achieved statements and variance reports, resolving critical issues by reporting them to the Senior Planner, and documenting progress meetings with Minutes of Meeting (MOM). Additionally, they generate MIS reports for management, contribute to the project closeout report, and maintain the project database with lessons learned. They also coordinate with contractors for timely deliverables and support data collection and issue resolution by collaborating with various stakeholders. Source: Adani Group | Job ID: 53616

1671 About the Role We are seeking a Senior Signal Integrity Engineer to develop, validate, and optimize high-speed signaling solutions across blade- and rack-level architectures for advanced compute platforms. This role sits at the intersection of silicon, package, interconnect, board, and system design , with a strong emphasis on hands-on measurement, simulation correlation, and cross-functional technical communication. Key Responsibilities End-to-end signal integrity analysis for blade- and rack-level system architectures. Analyze and optimize high-speed and low-speed I/O interfaces, including PCIe Gen4/5/6, Ethernet, DDR, SerDes, SPI , I2C, etc . and related interconnects. Perform time-domain and frequency-domain simulations using tools such as Ansys HFSS, Keysight ADS, Cadence Sigrity, CST, SPICE , or similar. Support hands-on lab validation using VNA, TDR, BERT, and high-speed oscilloscopes . Correlate simulation results with lab measurements to identify margin gaps, debug issues, and improve design methodology. Collaborate with silicon, package, board, connector, cable, and system design teams to optimize I/O channel performance. Work with interconnect vendors and ODMs to guide board layout, stack-ups, routing rules, and system design decisions. Review schematics, layouts, simulation results, and validation data for blade, backplane, and rack-level hardware. Prepare and communicate clear validation reports, measurement summaries, debug findings, and technical recommendations to internal teams, vendors, and senior technical stakeholders. Required Qualifications Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or a related field . Strong experience in signal integrity for high-speed digital systems. Hands-on measurement expertise using VNAs, TDRs, BERTs, and high-speed oscilloscopes . Experience measuring and analyzing S-parameters, impedance profiles, eye diagrams, jitter, timing margins, insertion loss, return loss, and cro