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Grafana Labs, the company behind the open observability cloud, is founded on the principles of open source, open standards, open ecosystems, and open culture. Grafana Cloud, our fully managed observability platform, is flexible and built for scale. With Grafana Cloud's actually useful AI, organizations can see, understand, and act on all their disparate data to move at the speed of their ambitions. Today, more than 35 million users and 7,000+ customers – including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce – trust Grafana Labs to ensure reliability of their applications and systems, resolve incidents quickly, and optimize their telemetry to reduce noise and cost. We are a 100% remote company with 1,600+ team members across 40+ countries, and we’re backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at and follow us on and _. We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. The Opportunity We build Tempo, the open-source distributed tracing backend behind Grafana Cloud Traces and Grafana Enterprise Traces (GET). Tempo makes it easy to search traces, generate metrics from spans, and connect tracing data with logs, metrics, and profiles across the Grafana stack. 2026 is an inflection point for Tempo. After a major architectural upgrade and the launch of TraceQL metrics, we are shifting from foundational work to product and operational excellence, and evolving Tempo from a SaaS database into a platform that powers Grafana’s next generation of observability products (App Observability, Asserts, Traces Drilldown, and AI-driven assistants). Over the next year, you will help us: • Make Grafana Cloud Traces “just work” for customers by eliminating rough edges, confusing limits, and hidden failure modes., • Achieve operational excellence at scale as we grow from close to 50 cells today into triple digits this year, with autoscaling, parameterized rollouts, and aggressive toil reduction., • Evolve Tempo into a platform enabler: higher-density APIs, trace aggregation, TraceQL metrics math, and machine/LLM-friendly interfaces that downstream products and agents can build on., • Push performance further: faster query latency at hundreds of MB/s ingestion and performant 30-day query ranges to match competitors., • Prepare Tempo for an agent-driven world: larger, burstier, higher-cardinality workloads, and new categories of AI-powered workflows, such as assistant-driven triage and “why is this slow?”- style investigations. What You'll Be Doing: As a Staff Engineer on Tempo, you will set technical direction on the hardest problems in our roadmap and raise the bar across the team. • Lead multi-quarter technical initiatives from problem framing through rollout, e.g., trace aggregation APIs, Limitless Tempo, autoscaling cells and customer limits, or query engine improvements., • Own the architecture of core Tempo components: ingestion, storage, query, and metrics generation. Drive design reviews, make sharp trade-offs on performance, cost, and complexity, and document the “why” for the team., • Design APIs for humans and agents. Shape the next generation of Tempo’s interfaces (structured, deterministic, discoverable) so that Act 3 products, LLM-driven assistants, and external integrators can build on Tempo reliably., • Drive operational excellence. Own outcomes against concrete SLOs (P99 write latency, incident recurrence, TCO per ingested GB) and push the team toward Zero Ops through automation, parameterized rollouts, and actionable alerts., • Partner with Product and sibling teams. Work closely with PMs and with App Observability, Asserts, Drilldown, and Grafana Assistant teams to understand how Tempo gets consumed and to ship what unblocks them., • Mentor engineers. Raise the engineering bar through code review, design feedback, pairing on hard problems, and writing that leaves the team smarter than you found it., • Participate in on-call for the services you help build, and be a force multiplier in incident response and post-incident learning., • Contribute to open source. Tempo is OSS. You will engage the community, review external contributions, and help steer the project in the open. We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups—always paired with strong code review and quality standards. You’ll also have access to frontier models (e.g., GPT-5.6, Claude Fable, Gemini Pro 3.1). Example problems you could work on: These are the kinds of projects landing in 2026. Any one of them is a Staff-sized problem: • Trace aggregation and higher-density APIs: extend TraceQL metrics, design LLM-friendly response types, and make Tempo a first-class data source for Grafana’s AI assistant., • Autoscaling end to end: customer limits and Tempo cells, with hysteresis, predictive scaling for spikes, and safe scale-down., • Agent-scale ingestion and query: guardrails for bursty, high-cardinality, agent-generated workloads., • Query performance: new data formats, smarter query pipelines, targeted optimizations for common Drilldown and Traces workflows, and 30-day query ranges., • Rollouts and multi-cell operations: parameterized rollouts, push-button deploys, and the tooling to grow safely into triple-digit cell counts without a proportional increase in alert noise., • Limits and self-service: drive customer-facing configuration and observability so escalations trend toward zero. What Makes You a Great Fit: • Technical leadership. A track record of leading complex, multi-quarter initiatives that spanned design, delivery, and operations, and made the teams around you better., • Deep systems experience. Substantial hands-on experience building and operating distributed data systems in production: ingestion pipelines, storage engines, query execution, or similar., • Strong software craftsmanship. You write clean, robust, performant software that others can maintain, and you know when to optimize vs. when to ship., • Strong Go, or a path to it. We write Tempo in Go. Deep experience in other systems languages (Rust, C, C++) translates well., • Operational mindset. You’ve owned production services, carried a pager, reduced toil, and treated SLOs as a product feature, not a chore., • Customer focus and pragmatism. You break complex problems into short feedback loops: analyze, design, deliver an MVP, learn, iterate., • Leadership through writing and collaboration. You lead through design docs, reviews, and shipped code, not hierarchy. You communicate clearly in a fully remote, asynchronous environment. Bonus Points for: • Experience with tracing, OpenTelemetry, or large-scale observability systems., • Experience designing query languages, SQL/TraceQL-like engines, or APIs intended to be consumed programmatically (by services or agents)., • Experience with columnar storage formats (e.g., Parquet) or purpose-built on-disk formats for analytical workloads., • Experience operating multi-tenant, multi-cell SaaS infrastructure at scale on Kubernetes., • Experience building for AI/LLM consumers: structured APIs, metadata/discovery endpoints, deterministic outputs, evaluation harnesses., • Open-source contribution or maintainership, and comfort engaging a community in the open., • Experience as an on-call user of Grafana, Prometheus, Loki, or Tempo in a previous role (or on a homelab)., • Experience in a fully remote, globally distributed team. How we work: We are a remote-first team that meets regularly over video and does most of our work asynchronously, in writing. We value creativity, diverse perspectives, and clear communication. Tempo is relied upon by prominent global organizations to monitor critical applications and infrastructure, and we expect everyone on the team, including our Staff engineers, to contribute ideas that make it a more reliable, more useful, and more loved product. Compensation & Rewards: In the United Kingdom, the Base compensation range for this role is GBP 103,958 - GBP 124,750. Actual compensation may vary based on level, experience, and skillset as assessed in the interview process. Benefits include equity, bonus (if applicable) and other benefits listed _. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: • 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose., • Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment., • Transparent Communication – Expect open decision-making and regular company-wide updates., • Innovation-Driven – Autonomy and support to ship great work and try new things., • Open Source Roots – Built on community-driven values that shape how we work., • Empowered Teams – High trust, low ego culture that values outcomes over optics., • Career Growth Pathways – Defined opportunities to grow and develop your career., • Approachable Leadership – Transparent execs who are involved, visible, and human., • Passionate People – Join a team of smart, supportive folks who care deeply about what they do., • In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it., • Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our .

Software Engineer (AI-Forward) As a Software Engineer with Texas Sports Academy, you play a key role in building the software that runs our school — student records, academic mastery tracking, training data, parent portals, admissions, and the AI-powered tools our guides and coaches use every day. This is a senior, AI-forward seat. You work directly with the founders, ship code every week with AI in your loop, and own the LLM-powered features that make our school feel nothing like a traditional school. What you will be doing • Designing, building, and shipping product features across the full stack every week, with AI coding tools running alongside you., • Building real LLM-powered product features: tutoring agents, parent-facing copilots, coach-facing dashboards, retrieval over student data, evals for every one of them., • Working directly with the founders on scope, architecture, and trade-offs — no PM layer in between., • Owning the reliability, security, and cost of both classical systems and LLM pipelines., • Running your own AI coding workflow — prompts, subagents, custom tools, MCP servers — and raising the bar on how the whole team uses AI., • Writing evals and regression tests for AI features the same way you'd write unit tests for classical code. What you will NOT be doing • Pretending AI is optional — if you're not already coding with Claude Code, Cursor, Codex, or an equivalent agent loop every day, this role is not for you., • Writing tickets for junior engineers to implement., • Sitting in status meetings all day — few meetings, more shipping., • Working on a narrow slice of a giant codebase. Key responsibilities • Ship production code and AI features real students, parents, and staff rely on every day., • Own at least one major system end-to-end., • Move fast — features go from idea to production in days, not quarters — without breaking things., • Raise the AI-engineering bar as the team grows., • Bachelor's or master's degree in Computer Science, Engineering, Math, or Physics., • 6+ years of full-time production engineering experience shipping software to real users at real scale., • At least one prior senior, staff, or tech-lead role., • Daily, fluent use of AI coding tools (Claude Code, Cursor, Codex, Windsurf, Aider, or equivalent) as your default way of writing software., • At least one shipped LLM-powered feature in production, with an eval story., • Strong skills in a modern web stack (TypeScript / React / Node or Python / Postgres / AWS or GCP)., • Excellent written English, • Legal authorization to work from India or Pakistan as a remote contractor with a US company. Nice to have • Master's or PhD from a tier-1 institution., • Former founder, first-five engineer at a funded startup, or shipped products that reached real users at scale., • Agent frameworks (LangGraph, CrewAI, Mastra, custom), vector search / RAG at scale, evals (Braintrust, LangSmith, custom), prompt caching, MCP servers, structured output / tool-use reliability, voice agents., • Public GitHub or a personal AI project we can actually try., • A personal project you built because you wanted to.

Flosum is a Salesforce-native DevOps and data protection platform serving enterprise Salesforce teams across the Fortune 5000. We are in wartime. Pipeline generation, retention, and product velocity are our three highest-priority problems, and AI is the lever that accelerates all three. We are hiring our first AI Go-to-Market Engineer to architect, deploy, train, and continuously optimize AI agents and automation across the entire revenue engine — from first touch to closed-won to expansion. This is not a "prompt engineering" role. This is a builder role. You will sit at the intersection of revenue operations, sales, marketing, and customer success, and your job is to make every human on the GTM team dramatically more productive by deploying AI systems that generate pipeline, accelerate deals, and reduce churn. You report directly to the CEO. WHAT THIS ROLE MUST DO TO CHANGE FLOSUM'S TRAJECTORY • Deploy and personally train at least one AI SDR agent within the first 30 days — focused on either inbound qualification from Qualified/Marketo or outbound prospecting via Outreach — and run weekly refinement cycles until it outperforms the bottom half of the human BDR bench., • Build an AI-powered inbound qualification system that captures, scores, and routes every website visitor and form fill in real time so no lead waits, no lead gets lost, and AEs only touch prospects that are ready to buy., • Increase revenue-generating time per AE from the current ~25% to 50%+ by automating CRM hygiene, call summarization (via Gong), follow-up sequencing, proposal drafting, and pipeline updates — eliminating every minute of admin work that doesn't directly produce revenue., • Architect the AI layer across Flosum's GTM stack (Salesforce, Marketo, Outreach, Qualified, Gong) so that data flows between systems without manual intervention, every customer touchpoint is captured, and AI agents share learnings in a virtuous loop with Salesforce as the hub., • Build and maintain an AI-driven competitive intelligence engine that monitors our competition, pricing changes, and product launches — and feeds structured insights to sales and product weekly., • Create hyper-personalized outbound campaigns at 10x current volume without proportional headcount increase — using AI to research accounts, generate personalized messaging based on ICP signals (Salesforce team size, current DevOps tooling, compliance requirements like DORA), and sequence multi-channel touches across email, LinkedIn, and phone., • Instrument a closed-loop data feedback system between BDR activity, pipeline outcomes, and AI agent performance so that ICP targeting, messaging, and channel mix improve every week based on actual conversion data — not guesswork., • Deploy AI-assisted customer health scoring and churn prediction models that flag at-risk accounts before renewal, feeding Terry's CS team with actionable intervention triggers tied to usage data, support ticket patterns, and engagement signals., • Prototype AI-powered product-led growth touchpoints — including conversational qualification on the website, self-serve onboarding assistance, and automated nurture for trial users — supporting the PLG motion as it matures., • Evaluate, select, and deploy no more than two AI vendors per use case using a structured bakeoff methodology: reference checks via email, FDE conversations before contract, and 30-day dedicated training commitments — no lazy agency deployments. Hands-on experience deploying and training AI SDR/BDR agents in a real B2B sales environment — not evaluating, not advising, actually deploying and iterating weekly for at least 90 days. Deep working knowledge of the modern B2B GTM stack — specifically Salesforce (as CRM and data hub), Marketo or equivalent MAP, Outreach or Salesloft, Gong or Chorus, and real-time chat/qualification tools like Qualified or Drift. Ability to write and maintain production integrations — Python, JavaScript, or equivalent — connecting AI agents to CRM, MAP, and sequencing tools via APIs. You don't need to be a software engineer, but you need to ship working code, not just configure no-code tools. Understanding of pipeline math and unit economics: you should be able to work backward from a revenue target to SQLs required, conversion rates by stage, and the specific AI interventions that move each number. If someone says "we need 67 SQLs per quarter at 20% win rate and $75K ASD," you should immediately know where AI can compress that funnel. Experience with AI agent orchestration — managing multiple AI agents working different segments or functions simultaneously, resolving conflicts, preventing duplicate outreach, and maintaining data quality in the hub (Salesforce). Demonstrated ability to build prompt libraries, training datasets, and refinement workflows for AI agents — treating agent training as a discipline, not a one-time setup. Comfort operating in a company doing ~$4M ARR with a small, high-intensity team. You will not have a large support staff. You will do the work yourself and scale through automation, not delegation. Strong opinions on when AI should replace human effort versus augment it — and the judgment to know that bad AI automation destroys deals faster than no automation at all. Familiarity with the Salesforce ecosystem — understanding of DevOps, CI/CD, data protection, and compliance workflows is a major advantage. You don't need to be a Salesforce admin, but you need to understand what our buyers care about and why Why Flosum • Category-defining opportunity: You will shape the future of how enterprises build on Salesforce. This is not incremental; it is transformational., • Direct CEO partnership: You will work shoulder-to-shoulder with a founder who is deeply technical, product-obsessed, and committed to building the best platform in the Salesforce ecosystem., • Fortune 100 customer base: Your product decisions will directly impact how the world's largest and most complex organizations operate., • AI inflection point: Agentforce and autonomous agents are rewriting the rules. Flosum is the first native platform to integrate these capabilities. You will define what comes next., • Missionary culture: We hire people who want to make a dent, not pad a resume. If you are looking for a place where your work matters and your ideas are heard, this is it. Compensation & Benefits • Competitive base salary + performance bonus, • Direct access to Salesforce ecosystem events, conferences (Dreamforce, TrailblazerDX), and customer engagement

Position: Mathematics AI Evaluator Type: Full-time or Part-time Contract Work Compensation: $73/hour Location: Geography restricted to USA, UK, Canada, EU Role Responsibilities Write and refine prompts to guide model behavior in mathematical contexts. Qualifications Must-Have PhD in Mathematics or a

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