Trainer Jobs

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. We are looking for an engineer with strong experience in machine learning and solid foundations in maths and computer science to join our growing Post-Training team at Baseten. Custom models are instrumental to the success of Baseten customers. By inference volume, the overwhelming majority of traffic at Baseten is to and from models that have been post-trained in some way, whether that be through reinforcement learning, supervised finetuning, a recent technique from the literature, or an in-house research technique from Baseten. The Post-Training team is responsible for the success of our customers’ post-trained models, and we employ a wide array of techniques to produce models that are more efficient and higher quality than even the biggest closed source models for the customer’s specific needs. Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often if involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels. RECENT RESEARCH Dense, on-policy or both? Repeated kv cache for long-running agents Distillation without the dark – rep

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? As a Member of Technical Staff in Data Analysis and Evaluation, you will play a pivotal role in ensuring the quality, reliability, and performance of our large language models (LLMs). Your primary focus will be on designing and conducting data collection tasks, assessing and evaluating dataset quality, and analysing the robustness and generalisability of our models. You will work closely with cross-functional teams, including researchers, engineers, and data annotators, to conduct data-driven decision-making and improve the overall effectiveness of our AI systems. This role combines expertise in statistics, experimental design incl. human annotators, and machine learning to ensure that our models are trained on high-quality data and perform reliably across diverse scenarios. You will contribute to Cohere’s mission of advancing AI by ensuring our systems are robust, scalable, and impactful. Please Note: We have offices in London, Paris, Toronto, San Francisco, and New York, but we also embrace being remote-friendly! There are no restrictions on where you can be located for this role. As a Member of Technical Staff

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 ClickUp is looking for a Senior Partner Sales Support Specialist based in the Philippines. This role is a critical enablement function supporting our Enterprise Partner Managers worldwide. You’ll be the operational backbone that keeps our partner sales motion running smoothly, helping Enterprise Partner Managers close deals faster by handling sales support activities across all regions. This is a high-impact, globally-scoped role that sits at the intersection of partner operations, sales enablement, and deal support. The candidate will be customer and partner-facing (on Zoom). The role: Provide day-to-day partner support to Enterprise Partner Managers across all regions (AMER, EMEA, APAC, LATAM), including deal registration processing, pipeline management, and opportunity tracking. Own and process smaller partner-sourced deals end-to-end, managing them through internal systems to ensure fast, accurate closure. Manage internal system administration (Salesforce, deal desk, CPQ) to remove friction from the partner deal cycle, including quote generation, order processing, and approval routing. Coordinate and manage partner onboarding logistics, ensuring new partners are set up in systems, trained on tools, and equipped to transact effectively. Maintain partner CRM data hygiene, ensuring accurate pipeline inputs, deal stage progression, and timely reporting for leadership. Act as the internal point of contact for partner deal escalations, liaising with Deal Desk, Legal, Finance, and RevOps to unblock and accelerate closures. Process MDF (Market Development Fund) submissions, track claims, and ensure timely

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. The Opportunity We're looking for an Analyst who thrives at the intersection of transportation economics, supply chain strategy, and quantitative modeling. This is a high-visibility individual contributor role—you won't be managing people, you'll be doing the work that directly shapes our network design, carrier strategy, and cost structure. In this role, you will not just build models, but proactively identify high-impact cost-saving and optimization opportunities, transforming raw analysis into business-critical strategy.Your analysis will inform senior leadership decisions on carrier RFPs, zone skip expansion, FC location strategy, and how we price and position our fulfillment product against competitors. You'll partner closely with carrier ops managers, supply chain leadership, data science, and commercial teams to turn complex data into clear, actionable recommendations. You Will Own lane analysis and zone skip strategy including lane-level margin modeling, utilization thresholds, go/no-go decision frameworks for trailer dispatch, and identification of new lane opportunities across our middle mile and deep injection network. Drive Cross-Functional Execut

About Mixpanel Mixpanel is the leading product intelligence and analytics platform, trusted by more than 29,000 companies to help understand how people use the products they build. By combining powerful analytics with AI that knows your business, Mixpanel helps teams see what’s working, diagnose what’s not, and decide what to build next. Learn more at mixpanel.com . About the Customer Engineering Team Mixpanel’s Customer Engineering teams are analytics consultants who embed themselves within our enterprise customer teams to drive business outcomes. We work with prospects and customers throughout the customer journey to understand what drives value, and serve as the technical counterpart to our Sales organisation to deliver on that value. You will partner closely with Account Executives, Customer Value Architects, Product, Engineering, and Support to successfully roll out self-serve analytics within our customers’ organisations, help manage change, execute on technical projects and services that delight our customers, and ultimately drive ROI on the customer’s Mixpanel investment. About the Role Our Customer Engineers are inquisitive, nimble, and able to clearly articulate the technical benefits and requirements of Mixpanel to developers and product managers, while also communicating the business value of our product to senior executives. In your first month, you’ll become a Mixpanel expert—both in features and functionality as well as implementation. You’ll shadow customer calls and demos with current Customer Engineers and Account Executives while learning to articulate our value proposition, and be trained on Mixpanel’s internal systems and tools. Within your first three months, you’ll be directly involved in deal cycles with Account Executives across the ANZ region. You’ll lead the technical qualification for customer use cases and deliver customised demos for prospects and existing customers, working directly with leadership at the prospect’s organisation to und

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Smartsheet is hiring a Senior Machine Learning Operations Engineer to architect our machine learning production lifecycle. Your mission is to maintain and deploy ML models to a scalable, reliable, and secure production environment. You will design and maintain the infrastructure, automation, and monitoring systems that ensure our AI products are high-performing and cost-effective. You will report to our Director, Analytics Engineering & Data Governance and work from our Bangalore, India office. You Will: Model and Pipeline Automation Automate the deployment and retraining of ML models, from training through to production inference, by building and managing complete CI/CD/CT (Continuous Training) pipelines, adhering to MLOps best practices. Build, fine-tune, or use pre-trained LLMs, deep learning models or traditional machine learning models. Evaluate and recommend AI or ML solutions for the product using any combination of vendor solutions and/or custom-built models. Governance & Compliance Implement model versioning, lineage tracking, and auditing to ensure compliance with security and ethical standards. Performance Monitoring Continuously monitor the health and performance of production machine learning models, proactively identifying and correcting model drift, staleness, and performance degradation. Incorporate user feedback for iterative improvements and manage necessary model retraining cycles. Cross-Functional Collaboration Act as the "glue" between Data Scientists (who build models

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

As a Staff Technical Program Manager, you will partner with key Engineering, Product, Product Design, Marketing, and Analytics stakeholders to deliver Observability features and platform capabilities for MongoDB. As a seasoned program leader, you will be responsible for one of our most mission-critical programs this fiscal year, and own executive level communication related to the program. We are looking to speak to candidates who are based in Dublin for our hybrid working model. The right candidate for this role will be: Experienced with 10+ years of working in an engineering organization leading complex cross-functional technical programs Experienced with 5 years of Software development background, with Cloud storage and compute products Experience with Service Oriented Architecture and Cloud Infrastructure Able to leverage their knowledge and experience to influence technical discussions, summarizing outcomes and next steps Skilled at communicating across a diverse set of engineers and stakeholders Hyper-organized and capable of coordinating across multiple independent work streams and organizations A role model for effective execution practices, driven by an attuned sense of priority and urgency Able to leverage their experience in program delivery to influence improvements to our tools, operations, and architecture Trained in working with project tracking software (e.g. Jira, Rally, MS Project) Familiar with MongoDB or a comparable technology Interested in business automation work such as scripting in Python, Google Apps Script and Slack. Position Expectations: Leverage technical acumen and analytical skills to drive engineering programs forward and maximize business value delivery Recognize patterns in a sea of information and take action accordingly Design, maintain, and improve the processes and tools that power program delivery Build strategic partnerships with Product and Engineering stakeholders Expand knowledge into new domains as called upon Act as a me

As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling Implement models, run experiments at scale, and profile for reliability, performance, and cost Build simulation environments and replay infrastructure for agent training and evaluation Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity Collaborate with Research Scientists, Product, and Engineeri

As a Sr. Staff Technical Program Manager, you will partner with key Engineering, Product, Product Design, Marketing, and Analytics stakeholders to conduct data-driven experiments and deliver features for MongoDB. As a seasoned program leader, you will be responsible for one of our most mission-critical programs this fiscal year, and own executive level communication related to the program. We are looking to speak to candidates who are based in Dublin or Cork for our hybrid working model, or remote within Ireland. The right candidate for this role will be: Experienced with 15+ years of working in an engineering organization leading complex cross-functional technical programs Experienced with 10 years of Software development background, with Cloud storage and compute products Experience with Service Oriented Architecture and Cloud Infrastructure Able to leverage their knowledge and experience to influence technical discussions, summarizing outcomes and next steps Skilled at communicating across a diverse set of engineers and stakeholders Hyper-organized and capable of coordinating across multiple independent work streams and organizations A role model for effective execution practices, driven by an attuned sense of priority and urgency Able to leverage their experience in program delivery to influence improvements to our tools, operations, and architecture Trained in working with project tracking software (e.g. Jira, Rally, MS Project) Familiar with MongoDB or a comparable technology Interested in business automation work such as scripting in Python, Google Apps Script and Slack Position Expectations: Leverage technical acumen and analytical skills to drive engineering programs forward and maximize business value delivery Recognize patterns in a sea of information and take action accordingly Design, maintain, and improve the processes and tools that power program delivery Build strategic partnerships with Product and Engineering stakeholders Expand knowledge into new

As a Sr. Staff Technical Program Manager, you will partner with key Engineering, Product, Product Design, Marketing, and Analytics stakeholders to conduct data-driven experiments and deliver features for MongoDB. As a seasoned program leader, you will be responsible for one of our most mission-critical programs this fiscal year, and own executive level communication related to the program. We are looking to speak to candidates who are based in Dublin or Cork for our hybrid working model, or remote within Ireland. The right candidate for this role will be: Experienced with 15+ years of working in an engineering organization leading complex cross-functional technical programs Experienced with 10 years of Software development background, with Cloud storage and compute products Experience with Service Oriented Architecture and Cloud Infrastructure Able to leverage their knowledge and experience to influence technical discussions, summarizing outcomes and next steps Skilled at communicating across a diverse set of engineers and stakeholders Hyper-organized and capable of coordinating across multiple independent work streams and organizations A role model for effective execution practices, driven by an attuned sense of priority and urgency Able to leverage their experience in program delivery to influence improvements to our tools, operations, and architecture Trained in working with project tracking software (e.g. Jira, Rally, MS Project) Familiar with MongoDB or a comparable technology Interested in business automation work such as scripting in Python, Google Apps Script and Slack Position Expectations: Leverage technical acumen and analytical skills to drive engineering programs forward and maximize business value delivery Recognize patterns in a sea of information and take action accordingly Design, maintain, and improve the processes and tools that power program delivery Build strategic partnerships with Product and Engineering stakeholders Expand knowledge into new

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Bridge is creating an entirely new payments platform, built with stablecoins, to simplify global money movement. Bridge enables faster, cheaper payments and borderless access to dollars via stablecoins. Through our APIs, businesses can send and receive funds across borders faster and cheaper than SWIFT and other fiat-only rails. Our virtual accounts enable international consumers and businesses to easily access, store, and spend US dollars. Our payouts infrastructure enables platforms to disburse USD to anyone globally. We believe many trillions of dollars will move and settle through stablecoin payment rails. Bridge is pulling this future forward. The Stripe Finance and Strategy team is an integral thought partner to Stripe's core functional leaders. We bring understanding and predictability to Stripe's bottom-line financials. We work cross-functionally across many surfaces at Stripe and help steward Stripe's resource investments to improve the durability of our long-term financial performance. This role will partner closely with Bridge, a small team of people experienced in building financial infrastructure at leading companies. What you'll do We are looking for a Strategic Finance Partner who combines the rigor of a trained financial analyst with the technical self-sufficiency to answer complex business questions independently. This is a high-ownership, high-exposure role. You will own business forecasting, pricing analytics, and cross-fu

About the Team The Artifacts team is building the AI-native creation layer for documents, spreadsheets, slide decks, dashboards, reports, analyses, and new forms of interactive work products. We are rethinking what creation looks like when models can move from an ambiguous user goal to a polished, editable artifact with strong structure, taste, correctness, and speed. This is a high-agency team working across product, infrastructure, and research. We partner closely with model training teams to shape how frontier models create artifacts, and with ChatGPT product teams to turn those capabilities into experiences that millions of people can use. The work spans full-stack product engineering, model integration, rendering and editing systems, collaboration, storage, evaluation loops, and production reliability. Our ambition is to build the premier product experience for AI-generated artifacts: starting with familiar work products like slides, sheets, and docs, then expanding into new artifact types that are only possible in an AI-native world. About the Role As Engineering Manager, Artifacts, you will lead and grow the engineering team responsible for building this product and technical foundation. You will manage a team of full-stack and infrastructure-oriented engineers, set technical direction, and stay hands-on enough to shape architecture and debug hard problems. This role sits at the intersection of product engineering, research, and infrastructure. You will partner with researchers on how models are trained and evaluated for artifact creation, with product and design on the user experience. This is a strong fit for a technical manager who wants to build and ship, not only coordinate. The team has a fast trajectory, so you will help define both the product surface and the team that builds it. In this role, you will: Lead, manage, and grow a team building AI-native artifact creation experiences across documents, spreadsheets, slide decks, and emerging artifact form

About the Team The Future of Computing Research team is an applied research team within the Consumer Devices group focused on developing new methods, models, and evaluation frameworks that support our vision for the future of computing. We work at the frontier of multimodal AI, helping turn emerging model capabilities into product experiences that are useful, delightful, and worthy of long-term trust. Our work explores a new class of AI systems that can learn over time, adapt to individuals, and support people in the flow of daily life. This includes long-term memory, user modeling, and personalization systems that are aligned not just with immediate satisfaction, but with a person’s broader goals, values, and well-being. We work closely across research, engineering, design, product, and safety to define what it means to build AI systems that know you over time, act at the right moment, and help in ways that are context-aware, respectful, and demonstrably beneficial. About the Role We are looking for a Research Engineer / Scientist to join the Future of Computing Research team to work on RLHF and post-training for personalized, multimodal AI systems. This role will focus on building the learning and evaluation foundations that help models become more context-aware, adaptive, and useful over time. You will work on problems such as reward modeling, preference learning, long-horizon evaluation, and policy improvement for systems that must make high-quality behavioral decisions in realistic user settings. The work is deeply product-grounded: success is not just higher benchmark performance, but better model behavior in real-world use. The ideal candidate is excited about pushing beyond one-turn assistant behavior toward systems that improve through feedback, learn from richer signals, and are trained against meaningful notions of user value. Internally, that maps closely to the need for careful reward design, feedback loops, and evaluation frameworks that test whether i