diff --git a/src/components/JobCard.astro b/src/components/JobCard.astro index ff6a7b5cf..e05df22cd 100644 --- a/src/components/JobCard.astro +++ b/src/components/JobCard.astro @@ -35,7 +35,21 @@ const { title, location, type, level, salary, description, responsibilities, min { responsibilities &&

Responsibilities

} diff --git a/src/content/config.ts b/src/content/config.ts index cb8055379..0efd50279 100644 --- a/src/content/config.ts +++ b/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(), diff --git a/src/content/sponsors/bloomberg/1-data-technologies.md b/src/content/sponsors/bloomberg/1-data-technologies.md new file mode 100644 index 000000000..79ed06c5d --- /dev/null +++ b/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" +--- diff --git a/src/content/sponsors/bloomberg/2-ai-app-enablement-observability.md b/src/content/sponsors/bloomberg/2-ai-app-enablement-observability.md new file mode 100644 index 000000000..fc41ae314 --- /dev/null +++ b/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" +--- diff --git a/src/content/sponsors/modal/1-python-sdk.md b/src/content/sponsors/modal/1-python-sdk.md index f8383ecab..1eef9e92c 100644 --- a/src/content/sponsors/modal/1-python-sdk.md +++ b/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:**