From 3ad4e37e96164eba45f106d2ed6ef5cf6f094719 Mon Sep 17 00:00:00 2001 From: hypha Date: Mon, 15 Jun 2026 14:34:52 +0100 Subject: [PATCH 1/5] add nested category/sub-item structure for job responsibilities --- src/components/JobCard.astro | 16 +++++++++++++++- src/content/config.ts | 5 ++++- 2 files changed, 19 insertions(+), 2 deletions(-) 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..2e4ce3bb4 100644 --- a/src/content/config.ts +++ b/src/content/config.ts @@ -252,7 +252,10 @@ 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(), From b767549a886edc2694df00b89e348e06bc947664 Mon Sep 17 00:00:00 2001 From: hypha Date: Mon, 15 Jun 2026 14:37:26 +0100 Subject: [PATCH 2/5] add bloomberg's jobs --- .../sponsors/bloomberg/1-data-technologies.md | 57 ++++++++++++ .../2-ai-app-enablement-observability.md | 92 +++++++++++++++++++ 2 files changed, 149 insertions(+) create mode 100644 src/content/sponsors/bloomberg/1-data-technologies.md create mode 100644 src/content/sponsors/bloomberg/2-ai-app-enablement-observability.md 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..ae4393a85 --- /dev/null +++ b/src/content/sponsors/bloomberg/1-data-technologies.md @@ -0,0 +1,57 @@ +--- +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..e0062e544 --- /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" +--- From 85ec6eb0cbfe64fc7408cd19750d0c9d16aa26d3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 15 Jun 2026 13:52:06 +0000 Subject: [PATCH 3/5] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/content/config.ts | 7 +- .../sponsors/bloomberg/1-data-technologies.md | 84 ++++++++++++++----- .../2-ai-app-enablement-observability.md | 16 ++-- 3 files changed, 73 insertions(+), 34 deletions(-) diff --git a/src/content/config.ts b/src/content/config.ts index 2e4ce3bb4..0efd50279 100644 --- a/src/content/config.ts +++ b/src/content/config.ts @@ -252,10 +252,9 @@ const jobs = defineCollection({ salary: z.string().nullable(), tags: z.array(z.string()).nullable(), description: z.string().nullable(), - responsibilities: z.array(z.union([ - z.string(), - z.record(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 index ae4393a85..79ed06c5d 100644 --- a/src/content/sponsors/bloomberg/1-data-technologies.md +++ b/src/content/sponsors/bloomberg/1-data-technologies.md @@ -1,57 +1,97 @@ --- -title: Senior Software Engineer - Data Technologies, Non-Securitised Data - Macro & Industries +title: + Senior Software Engineer - Data Technologies, Non-Securitised Data - Macro & + Industries location: London -type: +type: level: tags: -salary: +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. + 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. + 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. + 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 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. + + 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 + - 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 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 + - 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 + - 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 + - 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 + - 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 + - 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 index e0062e544..fc41ae314 100644 --- a/src/content/sponsors/bloomberg/2-ai-app-enablement-observability.md +++ b/src/content/sponsors/bloomberg/2-ai-app-enablement-observability.md @@ -1,10 +1,10 @@ --- title: Senior Software Engineer - AI App Enablement & Observability location: Dublin -type: +type: level: tags: -salary: +salary: benefits: description: " @@ -18,18 +18,18 @@ description: " **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. From ba980ae92923bbedbd377cd554cdd03515442638 Mon Sep 17 00:00:00 2001 From: hypha Date: Mon, 15 Jun 2026 15:11:47 +0100 Subject: [PATCH 4/5] add company blurb to modal's first job --- src/content/sponsors/modal/1-python-sdk.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/src/content/sponsors/modal/1-python-sdk.md b/src/content/sponsors/modal/1-python-sdk.md index f8383ecab..40d56fc2e 100644 --- a/src/content/sponsors/modal/1-python-sdk.md +++ b/src/content/sponsors/modal/1-python-sdk.md @@ -8,6 +8,19 @@ 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:** From b44c1a435709f7697ad72bb430ca8c60fc5faffa Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 15 Jun 2026 14:12:23 +0000 Subject: [PATCH 5/5] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/content/sponsors/modal/1-python-sdk.md | 29 +++++++++++++++------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/src/content/sponsors/modal/1-python-sdk.md b/src/content/sponsors/modal/1-python-sdk.md index 40d56fc2e..1eef9e92c 100644 --- a/src/content/sponsors/modal/1-python-sdk.md +++ b/src/content/sponsors/modal/1-python-sdk.md @@ -10,15 +10,26 @@ 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. + + 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:**