Production Supervisor Jobs

This is a core backend software engineering role (70% SWE / 30% Data) within our Mapping organization. You will focus primarily on building high-throughput services, distributed system architecture, and APIs, while leveraging large-scale spatial data to power Lyft's routing and mapping products. Note: This position focuses on backend service architecture and platform scalability rather than traditional ETL, BI, or data modeling pipelines. At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. As a Senior Software Engineer, Data on the Mapping team, you will collaborate with our world-class team of engineers, product managers, and scientists to grow and improve the quality of recommended routes and accuracy of our travel time estimations. You will lead the architecture and long-term technical direction of our offline experimentation tooling and route simulation services — the systems that let Lyft test routing changes safely before they reach production. You'll also build scalable data pipelines for experimentation, analytics, and machine learning models, along with the data governance and observability systems that keep them trustworthy. Your work will enable integration with partner teams and allow stakeholders across Engineering, Data Science, and Product to make data-informed decisions that directly impact Lyft’s growth and profitability. Our technology stack is based on the latest technologies such as AWS, Databricks, Kubernetes and Airflow. You will work with incredibly passionate and talented colleagues from software engineering, machine learning and data science on projects that directly impact millions of riders and drivers. Responsibilities Own core data pipelines end-to-end, building deep subject matter expertise in the systems you manage and defining/managing SLAs for pipelines, services, and datasets to ensure reliability at scale Serv

About us Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor and Clay Bavor . Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. What you’ll do Design and deliver production-grade AI agents : You'll build and ship highly performant, reliable, and intuitive AI agents that are central, mission-critical and drive revenue directly to Sierra's growth. These aren't prototypes—they are powerful, scalable systems running in production environments across industries like finance, healthcare, and commerce. Drive the Agent Development Life Cycle (ADLC) : You'll have complete ownership and autonomy from initial pilot through deployment and continuous iteration. You'll be responsible for building, tuning, and evolving AI agents in production environments, defining the standard for ADLC best practices along the way. Partner with large enterprises and cutting-edge startups: You’ll work directly wit

The mission of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for. About the Role, Mission or Department Overview The Research Director, Content Insights will have a critical role informing the creation, iteration and measurement of video and video products at the New York Times. You will lead all video measurement research to inform our understanding of how video content performs across platforms, surfaces, and audience segments. You will oversee the end-to-end measurement of video effectiveness, from individual videos to longer-form recurring concepts, and translate data into relevant strategies that inform production, distribution, and editorial decisions. You will also work with product, design, and editorial teams to improve on-platform video experiences. You will report to the Managing Director, News Products on the Audience Insights Team and will collaborate with partners across the Newsroom. You understand how to blend market research and user research methods and practices into actionable insights for an entire mission. This is a hybrid role based in our New York City headquarters. You can typically expect to come into the office 3 days per week. Responsibilities: Develop audience research to support the creation and growth of video franchises and recurring shows. Drawing on research methods used by production studios and streaming networks, you'll conduct research that can inform greenlight decisions — including concept testing, pilot testing, audience appetite studies, and competitive landscape a

About Ema Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale. The Role We are looking for an Engineering Leader to manage and scale multiple product lines in the Voice, BPO, and Workforce Management space. This is a high-impact leadership role that sits at the intersection of real-time voice systems, operations research, and data-intensive platform engineering. You will report directly to the Head of Engineering and own the engineering organization that builds the infrastructure powering Ema’s Voice AI Employees, Agent QA, auto-learning pipelines, rich analytics, and workforce optimization capabilities — all operating as scalable, multi-tenant systems deployed across global geographies. You will collaborate with Product, ML/AI, and Go-to-Market teams to translate customer needs into production systems that handle high volumes of voice data, deliver real-time insights, and continuously improve through automated learning loops. As the owner of multiple product lines, you will balance roadmap priorities across Voice, BPO operations, and WFM (work force management) — ensuring each product evolves cohesively while meeting distinct customer needs. What You Will Do Scalable Multi-Tenant Systems Architect and build multi-ten

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here). Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ 🌏 Location: You can choose to work in Qonto as long as you're living in (or willing to relocate to) either Germany, France, Italy, Serbia, or Spain. Mission: Join us as a Backend Engineer to build the financial infrastructure that 600,000+ European SMEs depend on every day, from highly scalable APIs to robust banking services that handle real money, in real time, with zero room for mistakes. ➡️ As a Backend Engineer at Qonto, you will Design, build, deploy, and maintain services handling real financial transactions, owning reliability in production, not just at merge time Co-own service architecture, resilience, and scalability with respect to Domain-Driven Design principles Grow in technical leadership: lead design discussions, anticipate risks, and mento

Banking hasn't changed much in decades. We're changing that. One product at a time. Lunar is a Nordic challenger bank building the future of financial life with AI at the core. Our engineers, designers, and product people work on problems that actually matter. Making money simpler, smarter, and more rewarding for millions of people across the Nordics. Fully licensed and founded in 2015, with offices in Copenhagen, Aarhus and Stockholm. We move fast, we build with purpose, and we're just getting started. As our new (Senior) Data Scientist you'll join our team based in Aarhus, reporting to Eugene, our Senior Engineering Manager, working to bring data and machine learning into how we prevent fraud, manage risk, and grow the business. What will you do? Design, build, and deploy machine learning and statistical models across fraud detection, transaction monitoring, loan risk assessment, customer risk scoring, and sales and lead generation Build and operate experimentation and evaluation frameworks — using tools like MLflow and Metaflow — with strong test sets and benchmarks across projects Dive into data analysis and feature engineering to keep pushing model performance further Partner closely with data engineers, analysts, and business stakeholders to define requirements and turn insight into decisions Document models, methodologies, and results clearly, and bring the latest ML and GenAI advances into how Lunar builds What are we looking for in you? Experience developing and deploying machine learning models in a production environment Proficiency in Python and relevant ML libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience with cloud-based ML platforms such as AWS SageMaker or Kubeflow Solid grounding in machine learning, statistical modeling, and data analysis techniques — experience in financial services or another regulated industry is a plus Strong problem-solving and communication skills, with the ability to translate technical concepts into business in

Who we are About the team Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants. Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers. What you'll do We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can im

Fiduciary Trust International is a premier investment and wealth management firm with a commitment to growing and protecting wealth across generations. We offer a dynamic and collaborative approach to managing wealth for high-net-worth and ultra high-net-worth individuals and families, family offices, endowments, foundations, and institutions. Our investment managers, tax and estate planning professionals work together to develop holistic strategies to optimize clients’ portfolios while mitigating the impact of taxes on their wealth. As a fiduciary, the guidance we provide is in the best interests of our clients, without conflict or competing benefits. We offer boutique customization and deep expertise in specialized investment, tax and planning strategies alongside sophisticated technology and custody platforms. Fiduciary Trust International is owned by Franklin Templeton, a dynamic firm that spans asset management, wealth management, and fintech, giving us many ways to help investors make progress toward their goals. With clients in over 150 countries and offices on six continents, you’ll get exposed to different cultures, people, and business development happening around the world. About the Department: Our AI Engineering team develops and scales production-ready Generative AI and Agentic AI solutions that help solve complex business challenges and accelerate innovation across the organization. The team brings together AI engineers, architects, and business stakeholders who work collaboratively to design, build, and deploy secure, enterprise-grade solutions on Microsoft Azure. Joining this team offers opportunities to work with emerging technologies, influence AI standards and best practices, and contribute to high-impact initiatives in a collaborative and growth-oriented environment. How Y

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Micron’s DRAM Design Engineering Group (DDEG) is where innovation meets excellence. We are advancing memory and storage technologies through collaborative engineering and creative problem-solving. Our team works at the forefront of semiconductor design, developing solutions that shape the future of memory products in a fast-paced, learning-focused environment. As a Design Verification Engineer, you will help develop next-generation memory technologies by verifying and optimizing digital and analog circuit designs. In this role, you will work closely with global multi-functional teams across the product lifecycle to deliver high-quality, manufacturable memory solutions that meet performance, reliability, cost, and customer requirements. Your work will directly contribute to bringing advanced memory products from concept to production. Responsibilities: Verify circuit functionality, reliability, power, and compliance with product specifications Drive verification planning, coverage closure, circuit debug, and design improvements Perform circuit modeling and simulation using industry-standard tools Support silicon validation, reticle experiments, and tape-out activities Partner with engineering, manufacturing, and product teams to deliver manufacturable designs Minimum Qualifications: Bachelor’s degree in Electrical Engineering or a related field 4+ years of semiconductor design, verifi

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Job Summary As a Real Time Defect Analysis Engineer, you will drive yield, quality, and process improvements in advanced semiconductor manufacturing through defect characterization, root cause analysis, and process optimization. Working closely with cross-functional engineering teams, you will investigate defectivity and process deviations, evaluate new materials and equipment, implement corrective actions, and develop solutions that enhance process capability, product performance, and manufacturing efficiency. Success in this role requires strong analytical and problem-solving skills, effective collaboration, and a passion for continuous improvement and technology innovation. Main Responsibilities Establish and improve process condition and technology. Upgrade process capability and reduce production cost. Establish and modify process management projects. Evaluation, promotion and planning of new equipment / materials. Abnormal analysis and improvement. Maximizes yield by working on tools, the process, or wafer fabrication recipes. Troubleshoots wafer fabrication problems and performs root cause analysis. Works with other proc

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Job Summary To lead engineering and maintenance works in the fields of Mechanical equipment and system on site, to achieve continual improvement to system robustness, reliability and efficiency with the necessary guidance and direction from Facilities organization, to achieve facilities department, site, facilities corporate and company goals. Main Responsibilities Plan and execute preventive and corrective maintenance programs for Facilities mechanical systems including HVAC, exhaust systems, scrubbers, process cooling water, chillers, boilers, cooling towers, compressed air systems, Fire protection system and cleanroom air systems to ensure maximum equipment uptime and reliability Respond and problem solving of system issues and production interruptions to mechanical related systems Plan, design and lead mechanical engineering projects includes new construction, system upgrading and quality improvement Perform capacity tracking and planning for mechanical systems Enforce to company’s environmental safety health policy and procedure, standard operating procedure with risk assessment, good housekeeping standard and quality policy when performing work. Utilize computer base software to do

About the role Chime is looking for an engineering leader to help manage and guide one of the Trust & Safety engineering teams. Chime’s Trust & Safety org is on a mission to protect our members and fuel durable growth. In practice, that means earning member trust through secure, seamless experiences; preventing fraud and abuse; reducing unnecessary friction; and meeting regulatory obligations while enabling responsible growth. We're hiring Tech Lead Managers (TLM) for multiple squads in Trust & Safety. TLMs at Chime manage a team of 3-5 engineers: Setting the hiring bar, coaching and developing talent, and driving all formal people management activities. They own execution and delivery for the squad. They stay in the code, authoring and shipping features alongside their team. In this role, you can expect to: Manage and develop a squad of 4-5 engineers: Hiring, 1:1s, career coaching, managing for performance Own delivery for your squad end to end, from sprint planning and delivery commitments through service SLAs and operational health Write and ship code alongside your team, authoring pull requests that land in production Be accountable for the squad's technical direction, architecture, and quality bar, either via a tech lead on the squad, or by taking on the tech lead role yourself. Use AI coding agents and tooling to raise both your own leverage and your squad's, and establish the practices that make that durable for the team Partner with Product and Design on scope, prioritization, and quarterly planning, surfacing technical dependencies and capacity needs early Act as the squad's cross-functional point of contact for its technical capabilities and dependency coordination, and carry the "what should we build" conversation if/when a PM isn't dedicated to your squad Identify technical debt and advocate for sustainable engineering, staying an active contributor to the codebase Ensure quality, performance, and operational health, including a

PagerDuty (NYSE:PD) is a leader in Digital Operations Management. In an always-on world, organizations of all sizes trust PagerDuty to help them deliver a perfect digital experience to their customers, every time. Teams use PagerDuty to identify issues and opportunities in real time and bring together the right people to fix problems faster and prevent them in the future. Over 13,000 organizations (including 60 of Fortune 100) rely on PagerDuty to succeed with Digital Transformation, Cloud Migration, and DevOps Modernization. Notable customers include GE, Cisco, Genentech, Electronic Arts, Cox Automotive, Netflix, Shopify, Zoom, DoorDash, Lululemon and more. We are expanding rapidly as a platform for Digital Operations Management using AI/ML and Automation and growing our adoption by Development, IT, Customer Service, Security, and other teams across the organization. PagerDuty is looking for a Machine Learning Engineer who is passionate about collaborating with data scientists, product managers and engineers alike. As part of our team, you will help us accelerate the development and extension of products powered by Gen AI and many other shapes of Machine Learning. You’ll be contributing hands-on to the development of the services and pipelines that enable multiple ML/AI features in our product. You will have the opportunity to collaborate with multiple organizations, taking input and guidance from your senior stakeholders and helping bring our initiatives to reality. You’ll succeed by showcasing excellent capacity to manage time, demonstrating emotional intelligence as you navigate stakeholder relationships, and by continuously improving your technical skill set. Key Responsibilities Build and improve the capabilities that enable and accelerate the production of machine learning (ML) and generative AI (genAI) based solutions Partner with data scientists, effectively sharing engineering context and collaborating to support larger initiatives Incorporate the best ava

PagerDuty (NYSE:PD) is a leader in Digital Operations Management. In an always-on world, organizations of all sizes trust PagerDuty to help them deliver a perfect digital experience to their customers, every time. Teams use PagerDuty to identify issues and opportunities in real time and bring together the right people to fix problems faster and prevent them in the future. Over 13,000 organizations (including 60 of Fortune 100) rely on PagerDuty to succeed with Digital Transformation, Cloud Migration, and DevOps Modernization. Notable customers include GE, Cisco, Genentech, Electronic Arts, Cox Automotive, Netflix, Shopify, Zoom, DoorDash, Lululemon and more. We are expanding rapidly as a platform for Digital Operations Management using AI/ML and Automation and growing our adoption by Development, IT, Customer Service, Security, and other teams across the organization. PagerDuty is looking for a Machine Learning Engineer who is passionate about collaborating with data scientists, product managers and engineers alike. As part of our team, you will help us accelerate the development and extension of products powered by Gen AI and many other shapes of Machine Learning. You’ll be contributing hands-on to the development of the services and pipelines that enable multiple ML/AI features in our product. You will have the opportunity to collaborate with multiple organizations, taking input and guidance from your senior stakeholders and helping bring our initiatives to reality. You’ll succeed by showcasing excellent capacity to manage time, demonstrating emotional intelligence as you navigate stakeholder relationships, and by continuously improving your technical skill set. Key Responsibilities Build and improve the capabilities that enable and accelerate the production of machine learning (ML) and generative AI (genAI) based solutions Partner with data scientists, effectively sharing engineering context and collaborating to support larger initiatives Incorporate the best ava

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Data Science is at the heart of Lyft’s products and decision-making. Data Scientists at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges - from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products. Driver Incentives Science owns the algorithms and systems behind incentive design, influencing driver engagement and marketplace efficiency — from real-time supply positioning to longer-horizon earnings and engagement programs. The team is responsible for designing pay and incentive mechanisms that are efficient and good for driver experience over the long run. As a Data Scientist specializing in Algorithms, you'll partner closely with product, engineering, and operations leaders to build and scale incentive systems, shape long-term mechanism design strategy, and deliver on critical business goals tied to marketplace efficiency and driver earnings. Candidates with strong optimization backgrounds — think mathematical programming, control theory, or operations research — are a great fit, though we welcome strong candidates from machine learning or causal inference as well. The ideal candidate thrives in a fast-paced environment and brings a hands-on, entrepreneurial mindset to drive results. Responsibilities: Collaborate with engineering and product teams to design, implement, and iterate on new features and algorithmic improvements for driver incentives and pay mechanisms. Design, develop, and deploy optimization models, algorithms, and systems for problems such as budget allocation, multidimensional cost-curve development, and incentive targeting. Write production model code; collabor