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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. The Principal Software Engineer Opportunity The team that built Jeeva AI is building Digital Workers, a new suite of autonomous agents for identity operations and security inside Okta. The team works directly with Todd McKinnon, Okta's co-founder and CEO, with the opportunity to bring this product to Okta's 20,000+ customers. You will help shape the product with the ownership and pace of a startup and the resources of a public company. You will build the harnesses and evaluations that make these workers reliable. They investigate risk, use tools, wait for approvals and complete tasks across enterprise systems. Bring depth in agent execution or model quality and help turn it into working software customers can trust. About You Production builder : You have shipped AI software and know what changes when real users depend on it. Careful evaluator : You investigate failures and use evidence to judge quality. Strong owner: You follow an improvement through code, evaluation, release and customer use. What you will be doing Build the agent harness for planning, tool use, memory, context compaction, interruption and recovery during long-running work. Integrate OpenAI and Anthropic model APIs. Select models using measured quality, latency and cost, with clear failure handling. Evaluate existing harnesses and build only the missing pieces. Keep the harness connected to AWS workflow execution and approval state. Keep authorization and sensitive writes behind applicati

Altana is the network for trusted trade. Our AI-powered product network empowers governments and businesses to build a more resilient and secure global economy while keeping trade flowing. The Opportunity at Altana The Implementation team is the tip of the spear in getting Altana's platform into production inside our customers' businesses. We own the path from signed contract to realized value: standing up integrations, wiring Altana into the customer's existing business processes, and making sure the insight we deliver actually changes decisions on the customer's side. We are a delivery organization with a technical core. We work directly with customer stakeholders — from trade compliance analysts to enterprise IT — and we work just as closely with Altana's engineering, data science, and product teams to translate what we learn in the field into what gets built next. You Will Design and deploy agentic AI systems Design, build, and deploy agentic AI workflows — multi-step agents, tool and function calling, and automations — that power the customer's business processes and act on real customer data Ground agents in customer context: connect them to the right data sources, APIs, and systems of record, and define the tools, permissions, and guardrails they operate within Write and refine the prompts, system instructions, and evaluation criteria that make an agent reliable enough to trust in production Build the orchestration and human-in-the-loop checkpoints that keep agentic workflows safe, auditable, and correctable in a customer's environment Monitor, evaluate, and iterate on deployed processes — tracking accuracy, cost, and latency, and tuning them as the customer's data and needs evolve Own technology implementation and delivery Lead customer implementations end to end: scoping, configuration, integration, validation, go-live, and handoff to steady-state support Configure and deploy Altana's platform capabilities against real customer data and real customer

Altana is the network for trusted trade. Our AI-powered product network empowers governments and businesses to build a more resilient and secure global economy while keeping trade flowing. The Opportunity at Altana The Implementation team is the tip of the spear in getting Altana's platform into production inside our customers' businesses. We own the path from signed contract to realized value: standing up integrations, wiring Altana into the customer's existing business processes, and making sure the insight we deliver actually changes decisions on the customer's side. We are a delivery organization with a technical core. We work directly with customer stakeholders — from trade compliance analysts to enterprise IT — and we work just as closely with Altana's engineering, data science, and product teams to translate what we learn in the field into what gets built next. You Will Design and deploy agentic AI systems Design, build, and deploy agentic AI workflows — multi-step agents, tool and function calling, and automations — that power the customer's business processes and act on real customer data Ground agents in customer context: connect them to the right data sources, APIs, and systems of record, and define the tools, permissions, and guardrails they operate within Write and refine the prompts, system instructions, and evaluation criteria that make an agent reliable enough to trust in production Build the orchestration and human-in-the-loop checkpoints that keep agentic workflows safe, auditable, and correctable in a customer's environment Monitor, evaluate, and iterate on deployed processes — tracking accuracy, cost, and latency, and tuning them as the customer's data and needs evolve Own technology implementation and delivery Lead customer implementations end to end: scoping, configuration, integration, validation, go-live, and handoff to steady-state support Configure and deploy Altana's platform capabilities against real customer data and real customer

Altana is the network for trusted trade. Our AI-powered product network empowers governments and businesses to build a more resilient and secure global economy while keeping trade flowing. The Opportunity at Altana The Implementation team is the tip of the spear in getting Altana's platform into production inside our customers' businesses. We own the path from signed contract to realized value: standing up integrations, wiring Altana into the customer's existing business processes, and making sure the insight we deliver actually changes decisions on the customer's side. We are a delivery organization with a technical core. We work directly with customer stakeholders — from trade compliance analysts to enterprise IT — and we work just as closely with Altana's engineering, data science, and product teams to translate what we learn in the field into what gets built next. You Will Design and deploy agentic AI systems Design, build, and deploy agentic AI workflows — multi-step agents, tool and function calling, and automations — that power the customer's business processes and act on real customer data Ground agents in customer context: connect them to the right data sources, APIs, and systems of record, and define the tools, permissions, and guardrails they operate within Write and refine the prompts, system instructions, and evaluation criteria that make an agent reliable enough to trust in production Build the orchestration and human-in-the-loop checkpoints that keep agentic workflows safe, auditable, and correctable in a customer's environment Monitor, evaluate, and iterate on deployed processes — tracking accuracy, cost, and latency, and tuning them as the customer's data and needs evolve Own technology implementation and delivery Lead customer implementations end to end: scoping, configuration, integration, validation, go-live, and handoff to steady-state support Configure and deploy Altana's platform capabilities against real customer data and real customer

About Flexport: At Flexport, we believe global trade can move the human race forward. That’s why it’s our mission to make global commerce so easy there will be more of it. We’re shaping the future of a $10T industry with solutions powered by innovative technology and exceptional people. Today, companies of all sizes—from emerging brands to Fortune 500s—use Flexport technology to move more than $19B of merchandise across 112 countries a year. The recent global supply chain crisis has put Flexport center stage as we continue to play a pivotal role in how goods move around the world. We are proud to have the support of the best investors in the game who believe in our mission, solutions and people. Ready to tackle global challenges that impact business, society, and the environment? Come join us. There is no MSSP and no tier-1 queue here. Detection & Response engineers own their detections end to end: you write them, you tune them, and your team is paged when they fire. The security team is spread across the globe with a follow-the-sun pager rotation so nobody is paged at 3am local. The adversaries are real. The business is growing fast and the threat surface is growing with it. Defining the necessary telemetry is part of the job. What You'll Do Detection engineering Build and tune detections across endpoint, identity, SaaS, and cloud , treating them as software: version-controlled, peer-reviewed, and shipped through the same CI/CD practices the rest of engineering uses. Track detection quality as measured quantities : coverage against MITRE ATT&CK, precision, time-to-detect. We don’t build-and-forget here. Response & automation Own EMEA-hours incident response: triage, contain, remediate, and write the retrospective that turns the incident into a systemic fix. Build automation that removes toil from investigations, and partner closely with the US-based team so context carries across time zones instead of getting lost at handoff. Telemetry &

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. With over half a billion rides and counting, Lyft is solving hard problems in a flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with petabyte-scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business. As a PhD Machine Learning Engineer Intern on our Applied AI team, you'll take on an open research problem tied to product experiences used by millions of riders. Working closely with a Staff ML Engineer mentor, you'll scope the problem, develop and evaluate new methods on real data, and take the work far enough that it can be shared with the research community, with the goal of a paper submission to a top ML venue. If you are a PhD student who enjoys turning open-ended research questions into working systems, and you want your research to be tested against real users and real data, this opportunity is for you! Responsibilities: Own a research project from start to finish: frame the problem, review related work, propose new methods, and design rigorous offline and online evaluations Design, build, train and test ML models in areas such as reinforcement learning, sequential decision-making, personalization Write production-quality code that turns research prototypes into working pipelines on Lyft's data and ML infrastructure Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame research questions within the business context Analyze experimental and observational data, and communicate findings clearly to both technical and non-technical aud

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. The Marketplace team at Lyft is responsible for accelerating the growth of the business while simultaneously delivering on key business and financial metrics, and Dynamic Pricing & Offer Selection is an integral part of that. Our team is the dynamic pricing team responsible for leveraging both real-time and historical data to set prices for every single ride at Lyft while balancing prices and ETAs to better serve our riders’ needs. We are looking for fast learning, data-driven individuals who are passionate about tackling these open-ended problems in pricing theory and making a large, positive impact in the business. In this role, you will work closely with Product and Engineering on a cross-functional team to prototype and build end-to-end dynamic pricing models that can operate at scale. In addition, you will be expected to leverage your domain knowledge and influence existing and future roadmaps to help Lyft continue to deliver a better experience for riders and drivers. Responsibilities: Partner with other scientists, colleagues in the pricing team, and with external teams to formalize problems mathematically and within the business context Perform complex data analysis to gain a deeper understanding of problems by identifying their root causes Develop and fit statistical, machine learning, or optimization models Write production code; collaborate with Software Engineers to ship models to production Design and implement both simulated and live experiments Analyze experimental and observational data; facilitate launch decisions Communicate findings to a broad audience consisting of Product, Engineering, and executive leadership Experience: Data Scientist with experience in Machine Learning, Statistics, Economics, or other quantitative fields M.S. or Ph.D. in Computer Science, Economics, Statis

About Us: Blockworks is an information platform that sits at the center of the crypto industry. We transform raw, complex data and facts into actionable research, trusted alpha-driven insights, and world-class events. The result is transparency and confidence. Blockworks connects investors and businesses in onchain capital markets. We give businesses a platform to earn trust and provide investors with the information they need to underwrite the asset class. The Role We’re hiring two BDRs to start our outbound team. The job is outbound. We sell data and software to fintechs, exchanges, banks, enterprises, and tech companies building in crypto. Your role is to find the right person at those companies and get them on a call with our sellers. We're scaling fast and many of our systems are still getting built. You'll figure a lot out on your own here. What You'll Do Run outbound on every channel: email, phone, X/Twitter, LinkedIn, Telegram, warm intros, etc Make cold calls and run the first qualification conversation yourself Book qualified meetings for our sellers and hand them off with enough context that the seller walks in prepared Leverage our DAS conference to generate qualified meetings for our sellers Write down what works. You’re the first two hires on our BDR team so we need to build a scalable system You're the Right Fit If You understand how to use AI to do more than just draft emails High volume outbound doesn't wear you down Your cold emails get replies You're comfortable on the phone with an executive Rejection doesn’t bother you You find crypto interesting enough to follow it on your own time [Ideally] You've carried an outbound meeting quota and hit it How You're Measured and Paid You're measured on qualified meetings, meaning meetings our sellers accept and that happen. Your variable pays on that same number and it's uncapped. Base salary of $60,000 - $70,000 with OTE of $90,000 to $110,000, depending on experience. You'll work directly with our sellers

About the Team OpenAI’s Business Marketing team helps organisations understand, adopt, and create value with AI. In EMEA, Consumer and Business Marketing plan as connected partners—bringing together audience insight, global narratives, product launches, customer proof, partnerships, field activity, and demand generation into coherent regional programs. We work closely with country and regional GTM teams, Global Integrated Marketing, and Production to build relevance, trust, adoption, and durable commercial impact across a diverse set of markets. About the Role We’re hiring an Integrated Marketing Manager to shape how businesses across EMEA understand, experience, and choose OpenAI. You’ll own end-to-end regional B2B programs and build them side by side with EMEA Consumer Marketing, so audience strategy, market priorities, and campaign sequencing work as one joined-up regional plan. You’ll translate global launches and campaigns into locally relevant market moments, and build EMEA-led programs where regional insight creates a stronger path to impact. The role has a dotted-line relationship to the Global Integrated Marketing lead and partners closely with Production: shaping global work early, recommending budget allocations across global, regional, and local activity, and turning strategy into decision-ready briefs with clear scope, timing, feedback, and approvals. You’ll also partner deeply with Sales and Marketing Operations. This is a hands-on role: you will set the strategy, write the brief, align contributors, unblock delivery, and stay close to performance and learning. In this role, you will: Own the EMEA B2B integrated marketing strategy and quarterly plan, working side by side with Consumer Marketing to align priority audiences, markets, launches, campaign sequencing, channels, and measurement. Turn audience insight, cultural and competitive context, product truth, customer proof, and business priorities into sharp regional strategies and creative briefs tha

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. The AI Agent & Experience team is Toast's internal platform team for AI. We build the infrastructure, governance, and developer tooling that makes AI a reliable, scalable force multiplier across the entire product development lifecycle. Our products include the LLM Proxy, AI Key Management, Observability pipelines, the Claude Plugin Marketplace, and autonomous agents that participate directly in the software development lifecycle. As a Senior Software Engineer on this team, you'll be hands-on building the systems that other Toast engineers depend on every day: the LLM proxy, agentic workflows, and the plugin marketplace that brings AI-native development practices to teams across the company. A day in the life (Responsibilities) Design, build, and ship features across AI platform infrastructure, including the LLM proxy, AI key management, and observability pipelines Develop and extend autonomous agents that participate in the SDLC, such as the AI Review GitHub App and Developer Platform MCP integrations Build on and contribute to the internal plugin marketplace, developing plugins that encode Toast's architectural standards and quality practices into AI assistant behavior Implement MCP (Model Context Protocol) services and service templates that make it faster and easier for teams across Toast to build AI-powered workflows Write high-quality, well-tested code and participate in design and code reviews Collaborate with engineers, product managers, and platform teams to understand developer needs and translate them into reliable platform solutions Contribute to the team's adoption of agentic development practices and tooling Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a c

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. With over half a billion rides and counting, Lyft is solving hard problems in a flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with peta-byte scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business. If you are a student with experience in machine learning workflows, passionate about solving challenging problems using data and working in a dynamic, creative, and collaborative environment, this opportunity is for you! Responsibilities: Contribute to the design, build, train and test of Machine Learning models Write production-level code to convert ML models into working pipelines Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context Analyze experimental and observational data, communicate findings to support decisions Participate in code and spec reviews to ensure code quality and distribute knowledge Experience: Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science or a related technical field from a university in Canada (required) , with a graduation date between December 2027 and Summer 2028 (required). For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should also have less than 2 years of relevant full-time work experience Available during Summer 2027 for the internship in Toronto Good understanding and knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, et

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. With over half a billion rides and counting, Lyft is solving hard problems in a flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with peta-byte scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business. If you are a student with experience in machine learning workflows, passionate about solving challenging problems using data and working in a dynamic, creative, and collaborative environment, this opportunity is for you! Responsibilities: Contribute to the design, build, train and test of Machine Learning models Write production-level code to convert ML models into working pipelines Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context Analyze experimental and observational data, communicate findings to support decisions Participate in code and spec reviews to ensure code quality and distribute knowledge Experience: Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science or a related technical field from a university in Canada (required) , with a graduation date between December 2027 and Summer 2028 (required). For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should also have less than 2 years of relevant full-time work experience Available during Summer 2027 for the internship in Toronto Good understanding and knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, et

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. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree at a university in Canada (required) in mathematical sciences ( Opera

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. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree at a university in Canada (required) in mathematical sciences ( Opera

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. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree in mathematical sciences ( Operations Research, Computer Science, Statistic