Production Tech Jobs

ABOUT CUBIST Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. ROLE Our research center in Singapore is seeking an experienced researcher with a strong background in alpha research. In this highly selective role you will have access to abundant research resources and exciting opportunities to discover high quality alpha signals that directly drive our investment decisions. RESPONSIBILITIES Conduct original quantitative alpha signal research Manage all aspects of the research process, including data analysis, alpha signal discovery, backtesting, trading idea generation, alpha signal/portfolio analysis and the management of production code Evaluate new datasets for alpha potential Follow, digest, analyze and improve upon the latest academic research DESIRABLE CANDIDATES 2+ years of research experience in Equities. Ph.D. or M.S. in finance, accounting, economics, mathematics, statistics, physics, computer science, operations research, or another quantitative discipline. Programming in any of the following: R, Python, or C++. Experience with SQL. Demonstrated ability to learn and apply new methodologies to alpha generation. Ability to work both independently and collaboratively within a team. Strong desire to deliver high quality results in a timely fashion. Detail-oriented. Willingness to take ownership of his/her work.

ABOUT CUBIST Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. ROLE Dynamically managing portfolio risk by evaluating historical and real-time strategy performance. Overseeing automated trade execution and monitoring transaction costs. Supervising a small team of researchers and developers on a daily basis. Designing, researching, and managing sophisticated investment strategies by creating and engineering advance quantitative financial computer modeling systems to aid in analysis and research. Performing research to acquire historical and production data sources needed to build investment models. Designing and developing quantitative mathematical algorithms to link the diverse data sets from various providers. Engineering investment models that will make the buy and sell recommendations for the portfolios using advanced quantitative mathematic statistics and investment theory to design and program strategies that explicitly forecast risk, return, and trading costs. Using quantitative models to value securities. Conducting ongoing, cutting-edge quantitative research and analysis to enhance existing strategies and to expand into new markets. Developing aspects of successful statistical models, focusing on forecasting and optimization. Expanding trading universe and volume and expanding to other exchanges and products. REQUIREMENTS Advance degree (Masters or Ph.D.) in a computational or analytical field. Minimum of 10 years’ experience developing, researching or implementing quantitative models for equities, futures and/or FX. Hands on experience with all aspects of the research process, including methodology section, data collection and analysis, testing,

ROLE We are passionate about data. We collaborate to build elegant, effective, scalable and highly reliable solutions to empower predictive modelling in finance. Cubist’s Data Services (CDS) group is looking for a Data Scientist to join our dedicated data team. Our group is responsible for the timely delivery of comprehensive and error-free data to some of the most demanding and successful systematic Portfolio Managers in the world. As a Data Scientist in the team, this individual will play a vital role in ensuring the smooth day-to-day implementation of a large research infrastructure, and the live production trading of billions of dollars of capital across global capital markets, including equities, futures, options and other financial instruments. RESPONSIBILITIES Onboarding novel datasets from a huge variety of sources into our platform Develop, test and deploy data pipelines, applications and services Re-shaping, aggregating, enhancing and creating features from datasets Engaging with vendors and internal stakeholders to understand characteristics of datasets Defining and automating qualitative data alerts and reports Partnering closely with investment teams to ensure their data requirements are met Perform preliminary analysis and research to be shared with investment teams REQUIREMENTS Masters in Financial Engineering, Statistics, Computer Science or other disciplines involving rigorous quantitative analysis Strong programming skills in Python and SQL Experience working with AWS, Linux and Airflow preferred but not required Financial industry experience preferred but not required Strong organization, communication and interpersonal skills Attention to detail and a love of processes Strong oral and written communication skills Ability to exercise sound judgment in assessing and determining how to handle queries, calls and issues Ability to multitask and prioritize assignments Commitment to the highest ethica

Agents have changed the game for software delivery and efficacy. Diligent is the leading GRC platform in the world, and we are racing ahead to take the agents show on the road and work with the customers where they work . The FDE function will lead the change on how we embed AI agents into some of the world’s most complex governance, risk and compliance environments. This is not a support or consultancy role. It is a builder role, for someone who is equally comfortable reading a failing agent trace, running a discovery workshop with a bank’s internal audit team, and translating what they find into a production-grade agentic solution. You will be building AI agents for GRC professionals , not assistants that surface suggestions, but agents that own complex, multi-step workflows end to end . Agents that customers can hand a task to and trust it will come back done. Closing the gap between a promising prototype and something a company Board and ELT depends on is a completely . Here’s a breakdown of what you’ll do Embed directly with major enterprise customers (global banks, regulated corporates) across EU and US ; sitting wi th internal audit teams, risk functions, compliance and governance professionals to understand their real workflows and devise agentic solutions to intelligently automate them creating tremendous efficacy and efficiencies for our customers. Run agent-focused discovery workshops, rapidly prototype agentic solutions, and test them with practitioners; distinguishing between workflows that need an agent and those that need a button. Source, integrate, and move data between enterprise systems as part of live customer implementations — understanding the real data landscape customers operate in and building reliable pipelines to support it . Take agents from prototype t

1743 - This position is in Austin, Texas. Position Summary We are seeking an experienced Board-Level Hardware Validation Engineer to define and execute the validation and verification of complex electronic systems throughout the product lifecycle. This role is responsible for defining validation strategies, developing test plans, executing hands-on testing, analyzing failures, and working directly with ODM partners to ensure products meet performance, reliability, quality, and compliance requirements before mass production. The ideal candidate combines strong electrical engineering fundamentals with practical lab expertise and is comfortable personally performing validation activities while coordinating with cross-functional teams and manufacturing partners. Key Responsibilities Validation Strategy & Planning Define comprehensive board-level and inter-board validation plans based on product requirements, design specifications, and customer use cases. Develop validation methodologies covering functional, electrical, thermal, power, signal integrity, reliability, and stress testing. Establish test coverage, acceptance criteria, qualification requirements, and release gates. Review hardware architecture, schematics, component specifications, and interface topologies to identify validation risks early in the design cycle. Define incremental validation and regression coverage for component substitutions, design changes, and firmware updates. Hands-On Validation Execution Develop, automate, and execute validation tests on prototype and production-intent hardware. Perform board bring-up, functional verification, electrical characterization, and system-level integration testing. Validate communication interfaces, control signals, and timing requirements. Verify power sequencing, reset behavior, leakage current, and recovery across operating states. Execute temperature and voltage corner testing against approved operating limits. Use oscilloscopes, logic an

About the Team The Code Quality team sits within the Developer Platform organization and owns the systems that keep DoorDash's codebase healthy and secure as it scales: static analysis, quality gates, test frameworks, regression infrastructure, and tooling. Our job is to make sure the signals engineers rely on before shipping — test results, coverage, performance feedback etc — are fast and trustworthy. The decisions we make about tooling and standards directly shape how confidently and quickly engineering teams at DoorDash can ship to production. About the Role We're looking for Software Engineers to help build and maintain the systems that validate code quality across DoorDash's engineering org, treating our tooling as a critical product for the engineers who rely on it every day: static analysis and quality gates, test frameworks and regression infrastructure. You’ll design the tooling and automation that will help derive trustworthy quality signals, integrate them into the development lifecycle, and make it easy for engineers to execute reliable, repeatable workflows. You will collaborate across the engineering org, partnering directly with the teams who use what you build to understand the accuracy, reliability and performance of their functionality. You will report into the Engineering Manager on our Code Quality team in our Developer Platform organization. You must be located in either San Francisco, CA, Sunnyvale, CA, Los Angeles, CA, Seattle, WA, or New York, NY. You're excited about this opportunity because you will… Build and maintain quality tooling — static analysis, quality gates, coverage reporting, test frameworks, regression infrastructure — and integrate it directly into our developer workflows and CI/CD pipelines Define and derive quality signals - flakiness, pass rate, coverage, performance, scale readiness etc - Build tooling that improves everyday engineering workflows, including local development, CI/CD, debugging, and rollou

About the Team The Consumer Engineering Team is responsible for helping consumers discover and order everything they love globally. Our work spans the entire consumer journey across homepage, search, store discovery, item exploration, checkout and post checkout. We aim to craft a hyper-personalized, delightful and frictionless experience for millions of our customers. About the Role As a Senior Staff Machine Learning Engineer on Core Cx, you will set the personalization (P13n) strategy for the entire consumer shopping journey and bring that strategy to life. You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across restaurant, grocery, retail and all business at DoorDash . You will modernize the recommendation system leveraging AI. You will demonstrate a strong command of production level machine learning, experience with solving end-user problems, and collaborate well with multi-disciplinary teams. You're excited about this opportunity because you will… Drive the engineering vision, strategy, and execution for an organization of 150+ Grow, build, and nurture impactful business-focused product engineering teams. Scale the team by developing leaders internally and attracting world-class talent Mentor and guide a fast-growing organization in setting the right architectural patterns, working with various vendors in the space, and making judicious investments in the right areas anticipating what the company needs a few years down the road. Partner with Business, Product, and other Engineering teams to transform DoorDash from local commerce to agentic commerce We're excited about you because you have… B.S. or M.S. in Computer Science or equivalent. 10+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. Proficiency in using AI coding tools (e.g., Claude Code) in th

About Vercel: Vercel is the agentic infrastructure company. We free people and agents to ship what’s next. For more than a decade, Vercel has shaped how the web is built. As the team behind Next.js, v0, and AI SDK, we create products that help builders move from idea to production with speed, security, and exceptional developer experience. Now, software is entering a new era, and the next generation of products will not just be used by people. They will be built, extended, and operated by agents. We are building the platform for that future, trusted by companies like OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide . Whether you’re building our products, supporting our customers, growing our community, or shaping our story, you’ll help define what comes next. About the Role: Vercel is scaling fast, and the Senior Manager, Accounting will be a critical hire as we build out the financial infrastructure that supports our global growth and path to the public markets. You will own end-to-end accounting operations for Vercel's statutory accounting across current and future entities, from monthly close and statutory compliance to audit support and process improvement. Working closely with the Head of Finance, India Center of Excellence, the US-based Controller and global finance team, you'll bring rigor, ownership, and a builder's mindset to a function that is growing quickly. This role is fully remote from your home office in India, with regular overlap hours with the US team. You will report to the Head of Finance, CoE who is also based in India. What You Will Do: Lead the full-cycle monthly and quarterly close for Vercel's current and future international entities, setting clear standards for accuracy, completeness, and timeliness in accordance with both local and US GAAP. Guide and support accounts payable and accounts receivable teams for correct accounting, manage payroll accounting and intercompany transactions in coordinat

Job title: Liquidity reporting Compliance Finance has mobilised a multi-year Integrity of Regulatory Reporting (IRR) programme to ensure that Regulatory Reporting across the Group is fit for purpose; accurate, timely and complete. The IRR programme is responsible for overseeing the design and implementation of a set of standards and control outcomes that will be consistently applied across the end-to-end Regulatory Reporting Process. IRR will drive the interconnectivity across various strategic transformation programs to ensure linkages and dependencies are well understood and aligned to the IRR standards and control outcomes. The Liquidity Product Based Review (LIQ_PBR) workstream is part of the overall data controls framework for Liquidity Regulatory Reporting. Product Based Reviews enable Finance to develop key assurance that the end to end data flow for Business Data Elements (“BDE’s”), from upstream Primary Booking Systems to downstream Reporting data warehouses (including the Liquidity Reporting Finance Data Application / Platform) complies with the applicable group data standards and controls (‘DMOV’ and ‘DUSE’), is complete and valid for the production of key Liquidity reports and metrics and that data quality or completeness issues discovered through the PBR process are appropriately logged, reported, escalated and managed to resolution. The LIQ_PBR Data Analyst contributes to Global Finance, supporting data flow discovery and documentation, from upstream primary trading and booking systems (PTS) to downstream Liquidity reporting platforms. The role holder will support the Liquidity PBR workstream to undertake quantitative and qualitative data documentation, test case formulation, data test execution and results tabulation, in order to provide the necessary assurance for management with regards to the traceability, lineage, transformation and controls on Liquidity business data elements from source systems to reporting. In this regards, the LIQ PBR d

About the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role We are looking for an experienced Mechanical Engineer with 7+ years of experience in design of IT hardware from chip/package to system levels. You’ll work alongside experts in thermal, mechanical, electrical, software, and systems engineering to support the design, analysis, and validation of mechanical and thermal systems that ensure the reliability, efficiency, and longevity of mission-critical hardware. This position requires strong analytical skills, hands-on testing experience, and the ability to work in a fast-paced, cross-disciplinary environment. 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: Lead mechanical design for AI supercomputer product in the data center application Collaborate with the cross functional team to design and optimize thermal solutions for data center hardware, including chips, power modules, and system-level cooling architectures Collaborate with cross-functional teams to integrate thermal management strategies into hardware design, from concept to mass production Design and validate mechanical systems, including chassis, enclosures, cooling systems, and high-power connections, ensuring alignment with performance and reliability standards. Perform 3D modeling, FEA, tolerance analysis, and prototyping, ensuring manufacturability and a

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We're seeking a Software Engineer to join our First-Party Hardware team. In this role, you will design, build, integrate, and validate the software used to manufacture, qualify, and deliver our hardware from the factory. You will work across the stack to create the infrastructure that runs internally and externally to coordinate all aspects of the production process. You will create the critical tools and procedures to execute, capture, process, and present the data resulting from the end to end assembly and validation of our hardware across multiple vendors and sites. This role is hands-on and high-ownership. You will work closely across teams both internal and external to define the standards that will be used across our products to ensure the velocity and quality of our 1P hardware. You will own the implementation, deployment, and output of these systems as well their continued maintenance and SLAs. Location: San Francisco, CA (Hybrid: 3 days/week onsite). Relocation assistance available. In this role, you will: Design, develop, and maintain the software infrastructure for manufacturing process execution and data export. Own integration across internal customers and vendor systems and processes. Build and maintain the CI, release, and delivery pipeline of tooling to external partners. Build and maintain internal systems to ingest, process, deliver, and visualize critical data for internal teams and systems. Build system health monitoring, telemetry, remote d

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. About Talent at Modal Modal is growing fast, and the programs that bring people in and set them up to succeed are still being built. You'll join the Talent team as one of its first hires focused purely on programs; working closely with recruiting and leadership to build the events, internship, and campus presence that shape how the best people discover and experience Modal for the first time. The Role As Talent Programs Manager, you will own Modal's talent events, our intern program, and our presence at career fairs, end-to-end. This is a build-from-the-ground-up role for someone who wants full ownership rather than an existing playbook to execute. You'll work directly with recruiters, hiring managers, and marketing to make sure every program ladders up to real hiring outcomes, and you'll be the person who makes candidates' and interns' first experience of Modal a great one.

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We are seeking a highly skilled Physical Design Engineer with deep expertise in physical design and methodology. This individual contributor role sits within our physical design team and is central to delivering power, performance, and area (PPA) optimized datapath and interconnect solutions for next-generation AI accelerators. You’ll work closely with RTL designers to define and execute on physical design strategies. You will develop tools, flows and methodologies to increase team productivity. Your work will directly impact silicon’s performance and cost efficiency, as well as the team’s execution velocity and quality. In this role, you will: Develop, build and own tools, flows and methodologies for physical implementation Own physical implementation of floorplan blocks from floorplanning to final signoff Collaborate with RTL designers to drive optimal block implementation solutions Analyze and optimize design for timing, power, and area trade-offs, working in collaboration with EDA vendors and ASIC partners Qualifications: BS w/ 4+ or MS with 2+ years or PhD with 0-1 year(s) of relevant industry experience in physical design and methodology development Demonstrated success in taping out complex silicon designs Hands-on experience with block physical implementation and PPA convergence Strong coding experience with python, bazel, TCL Strong experience building physical design tools, flows and methodologies Strong understanding of microarchitecture, RTL design,

Datadog AI Research — Scholars Program with Carnegie Mellon University Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR. You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions. The Lab And The Research: DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact. Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure. What You'll Do: Own a research project end to end: framing the question, running the experiments, writing it up Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab Publish, and use the work toward your dissertation See research reach production, when it works Who You Are: Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field Depth in at least one area relevant to the research above Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers Evidence you can do research: conference or workshop papers, preprin

Location Details: India, Remote At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​ This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. Join Our Team... Here at GoDaddy, the ML Engineering (MLE) team exists as the backbone of our machine learning infrastructure, enabling ML scientists and product teams across Domains to ship models to production reliably, efficiently, and at scale. This team owns the full lifecycle of ML systems — from CI/CD pipelines and model serving infrastructure to GPU workload orchestration and observability. Through disciplined engineering practices, thoughtful system design, and close collaboration with ML scientists, data engineers, and product teams, we deliver the platform that powers domain search, pricing, recommendations, and emerging AI experiences for millions of customers worldwide. We are currently looking for an experienced, highly motivated Senior Engineering Manager to lead our ML Engineering team based in India. This is an established team with existing engineers — we expect the candidate to ramp up quickly on our ML infrastructure stack, build strong relationships with the team, and partner with both India-based teams and US-based teams to drive execution and grow the team further. This individual will join us on our journey to build and scale ML infrastructure that serves real-time predictions at low latency, automates model deployment and promotion, and provides the observability and reliability guarantees that production ML systems demand. Become part of a team that bridges the gap between ML research and production engineering — shipping systems that directly impact GoDaddy's core revenue. What you'll get to do... Lead a team o