Ep2026 sponsor jobs bloomberg

src/components/JobCard.astro

@@ -35,7 +35,21 @@ const { title, location, type, level, salary, description, responsibilities, min
     { responsibilities &&
       <h3 class="text-xl font-semibold mb-2">Responsibilities</h3>
       <ul class="list-disc list-inside mb-4">
-        {responsibilities.map((item:string ) => <li>{item}</li>)}
+        {responsibilities.map((item) => {
+          if (typeof item === "string") {
+            return <li>{item}</li>;
+          }
+          const category = Object.keys(item)[0];
+          const subItems = item[category];
+          return (
+            <li>
+              <strong>{category}</strong>
+              <ul class="list-disc list-inside">
+                {subItems.map((sub) => <li>{sub}</li>)}
+              </ul>
+            </li>
+          );
+        })}
       </ul>
     }
 

src/content/config.ts

@@ -252,7 +252,9 @@ const jobs = defineCollection({
     salary: z.string().nullable(),
     tags: z.array(z.string()).nullable(),
     description: z.string().nullable(),
-    responsibilities: z.array(z.string()).nullable(),
+    responsibilities: z
+      .array(z.union([z.string(), z.record(z.array(z.string()))]))
+      .nullable(),
     min_requirements: z.array(z.string()).optional().nullable(),
     requirements: z.array(z.string()).nullable(),
     preferred: z.array(z.string()).optional().nullable(),

src/content/sponsors/bloomberg/1-data-technologies.md

@@ -0,0 +1,97 @@
+---
+title:
+  Senior Software Engineer - Data Technologies, Non-Securitised Data - Macro &
+  Industries
+location: London
+type:
+level:
+tags:
+salary:
+
+benefits:
+description: "
+  Bloomberg is foremost a data company. Data is at the heart of everything we
+  do; we collect it, cleanse it, enrich it, derive it, validate it, and make it
+  available to our clients. This data is vast and varied and critical not only
+  to our success but to that of our diverse global client base, and we
+  continuously challenge ourselves to do this better and faster.
+
+
+  We are a team of software engineers who build and maintain data pipelines for
+  Bloomberg's Macro business areas, including Economics, Energy Transition,
+  Physical Assets & Geo, Commodities & Carbon, and Macroeconomic Analysis.
+  Working in close partnership with Bloomberg's Data department, we ensure that
+  critical data and metadata flows reliably from source systems into Bloomberg's
+  products, spanning ingestion, transformation, standardisation, enrichment, and
+  downstream integration.
+
+
+  Our engineers take ownership of their projects end-to-end, managing both
+  technical delivery and stakeholder relationships. We also partner closely with
+  infrastructure teams, contributing application-level insights that help guide
+  platform improvements and influence infrastructure strategy. The breadth of
+  domains we support gives you exposure to a broad and varied problem space.
+
+  We care deeply about engineering craft. We build reusable components and
+  shared libraries that solve cross-domain problems, abstracting away complexity
+  so that common patterns are handled well in one place rather than duplicated
+  across pipelines. We look for recurring challenges and invest in building
+  tools that improve reliability, reduce risk, and make our teams more
+  productive. This mindset means we are always looking for opportunities to
+  raise the bar on performance, code quality, and maintainability.
+
+
+  We are actively exploring how agentic and generative AI can augment our data
+  workflows to improve data coverage and quality. We are also contributing to
+  Bloomberg's semantic data initiatives, helping define how data flows into
+  Bloomberg's enterprise knowledge graph.
+
+
+  We value incremental delivery over big-bang releases. Getting our work into
+  the hands of users early helps ensure we are building what the business needs.
+  We foster a culture of psychological safety, collaboration, and continuous
+  learning, where it's safe to ask questions, challenge ideas, and support each
+  other to deliver under pressure.
+  "
+
+requirements:
+  - Strong backend experience with Python
+  - A degree in Computer Science, Engineering, Mathematics, a similar field of
+    study, or equivalent work experience
+  - Experience building and maintaining data pipelines or ETL workflows
+  - Good system design and architecture skills
+  - Experience working with large distributed systems
+  - Experience of working with Kafka pipes
+  - Experience of working with high volume, high throughput, scalable data
+    pipelines
+  - Experience working with big data pipelines and stores
+  - An understanding of continuous integration principles and writing testable
+    code
+  - Experience using Linux/Unix
+
+preferred:
+  - Experience integrating AI or machine learning into data pipelines or
+    developer tooling
+  - A track record of leveraging AI to improve personal or team productivity
+  - Familiarity with event-driven architectures and message-based data
+    processing
+  - Experience with data modelling or schema design
+  - Comfort working with diverse groups of stakeholders, both technical and
+    non-technical
+  - A desire to get involved in department and company-wide initiatives
+
+responsibilities:
+  - Take ownership of projects and drive them from design through to delivery
+  - Build robust, scalable data pipelines that process large volumes of complex
+    data reliably
+  - Identify recurring problems across domains and build reusable solutions that
+    benefit multiple teams
+  - Develop strong working relationships with engineering peers, data teams, and
+    business stakeholders
+  - Champion engineering best practices, writing well-tested, maintainable, and
+    high-quality code
+  - Deliver incrementally in a fast-paced environment, prioritising thoughtfully
+    across competing workstreams
+
+apply_link: "https://bloomberg.avature.net/careers/JobDetailPartner?jobId=17981"
+---

src/content/sponsors/bloomberg/2-ai-app-enablement-observability.md

@@ -0,0 +1,92 @@
+---
+title: Senior Software Engineer - AI App Enablement & Observability
+location: Dublin
+type:
+level:
+tags:
+salary:
+
+benefits:
+description: "
+  Platform Engineering builds the core platforms, tooling, and paved roads that Bloomberg engineers rely on to ship reliable, secure, and high-performing systems at scale.
+
+
+  The AI App Enablement & Observability team accelerates how AI products are built across Bloomberg Industry Group. Our mission is to make AI systems reliable, performant, cost-efficient, and continuously improving through platform tooling, deep observability, and automated feedback loops.
+
+
+  We build developer-facing platforms and workflows that enable teams to experiment, deploy, and operate AI and agent-based systems with confidence. This includes LLM gateways, agent platforms, benchmarking systems, telemetry pipelines, and self-improving infrastructure that closes the loop between observability and action. We emphasise strong developer experience, intuitive APIs/SDKs, and end-to-end ownership.
+
+
+  **What’s in it for you?**
+
+
+  You will help define how Bloomberg Industry Group builds and operates AI systems at scale by working on platforms that:
+
+  - Accelerate AI product development through reusable tooling and paved roads
+
+  - Provide end-to-end observability across AI systems (models, agents, pipelines, applications)
+
+  - Enable self-improving systems through telemetry-driven feedback loops
+
+  - Optimise cost, performance, and reliability of AI workloads
+
+  - Support both production AI systems and internal engineering agents
+
+You’ll collaborate across AI product, infrastructure, and platform teams to deliver foundational systems.
+
+  "
+
+requirements:
+  - Demonstrated experience building production software or platform systems
+  - Strong engineering fundamentals with distributed systems or backend platforms
+  - Experience or strong interest in observability and debugging complex systems
+  - Experience or strong interest in AI/ML systems, LLMs, or agent-based architectures
+  - Strong ownership mindset and ability to drive ambiguous problems to production
+  - Hands-on experience with modern agentic coding tools (e.g., Claude Code, Codex CLI, Cursor) and multi-model workflows
+  - Working knowledge of agent architecture internals (context engineering, tool loops, sub-agent orchestration)
+
+preferred:
+  - Experience with OpenTelemetry and modern observability ecosystems, including instrumentation, collectors, exporters, and tools like Prometheus, Grafana, and tracing/log systems
+  - Experience designing and operating telemetry pipelines, including sampling, retention, cardinality, and cost tradeoffs, as well as integrating observability into CI/CD and developer workflows
+  - Familiarity with AI/agent frameworks, including instrumentation of LLM calls, tool usage, workflows, and evaluation signals (quality metrics, benchmarking, regression detection)
+  - Experience building cost monitoring, forecasting, and optimization systems for AI workloads
+  - Familiarity with cloud and infrastructure tooling (e.g., AWS, Azure, Kubernetes, Terraform)
+  - Experience with agentic infrastructure concepts such as MCP servers, hooks, skills, subagents, sandboxing, and persistent memory patterns
+  - Active engagement with the agentic engineering frontier, including emerging patterns (e.g., harness vs. model, review debt, feedback loops)
+  - Demonstrated agent-native development practices (iterating with agents using testing, verification, and feedback loops)
+  - Strong security awareness for autonomous systems, including sandboxing, prompt injection risks, credential exposure, and guardrails
+
+responsibilities:
+  - Platform & Enablement:
+    - Build and evolve AI platform tooling (e.g., developer workflows, benchmarking systems)
+    - Design developer-friendly APIs, SDKs, and interfaces
+    - Contribute to systems across the Model Development Lifecycle (experimentation, deployment, evaluation)
+
+  - Observability & Telemetry:
+      - Build and operate observability platforms and telemetry pipelines (logs, metrics, traces, events)
+      - Provide visibility into latency, token usage, cost, quality, drift, and reliability
+      - Define instrumentation standards, schemas, and conventions
+      - Implement distributed tracing using modern approaches (e.g., OpenTelemetry)
+
+  - AI System Insights & Debugging:
+    - Enable end-to-end debugging of AI and agent workflows (model calls, tool usage, retrieval, orchestration)
+    - Build benchmarking, regression detection, and performance analysis capabilities
+    - Support observability for both production systems and internal engineering agents
+
+  - Closed-loop Optimization & Automation:
+    - Develop systems that turn telemetry into action (automated experimentation, regression detection, alerting)
+    - Build feedback loops that continuously improve model quality and system behavior
+    - Enable self-healing and self-optimising workflows
+
+  - Cost, Performance & Reliability:
+    - Build tooling for cost visibility, forecasting, and optimization
+    - Define SLOs, alerting, and performance tuning practices
+    - Improve reliability and scalability of AI infrastructure
+
+  - Ownership & Collaboration:
+    - Own projects end-to-end (RFCs, architecture, implementation, rollout, production support)
+    - Partner with AI teams to drive adoption of platform tooling and standards
+    - Produce high-quality documentation and improve developer experience
+
+apply_link: "https://bloomberg.avature.net/careers/Public?jobId=18854"
+---

src/content/sponsors/modal/1-python-sdk.md

@@ -8,6 +8,30 @@ salary: $150K – $350K • Offers Equity
 
 benefits:
 description: "
+  **AI needs a new infrastructure layer. We're building it at Modal.**
+
+
+  Every era of computing brought new workloads that previous infrastructure
+  couldn't support: mainframes, databases, and the cloud. Each time, the company
+  that rebuilt the layer underneath defined the decade. AI is no different,
+  except it touches everything instead of one slice, and the window to build the
+  layer underneath it is open right now.
+
+
+  Our customers include category-defining companies like Lovable, Ramp,
+  Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access,
+  sub-second container starts, and native storage, so it's simple to serve
+  low-latency inference, fine-tune models, and access production-ready sandboxes
+  at scale.
+
+
+  We recently raised a $355M Series C at a $4.65B valuation, led by General
+  Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold
+  since September. Our team includes creators of popular open-source projects
+  (e.g.,Seaborn, Luigi), academic researchers, international olympiad medalists,
+  and experienced engineering and product leaders with decades of experience.
+
+
   **The Role:**