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Bloomberg

Senior Software Engineer - Data Technologies, Non-Securitised Data - Macro & Industries

London

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.

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

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

Nice-to-Haves

  • 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
Apply Now

Bloomberg

Senior Software Engineer - AI App Enablement & Observability

Dublin

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.

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

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)

Nice-to-Haves

  • 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
Apply Now

Modal

Member of Technical Staff - Python SDK

Full time • New York; Stockholm

$150K – $350K • Offers Equity

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:

We’re looking for strong engineers with experience building developer tools that users love to work with. Our ideal candidate is someone with a demonstrated drive to build beautiful interfaces that enhance developer productivity.

Requirements

  • 5+ years of experience developing high-quality Python libraries with broad user-bases, ideally including some experience maintaining open-source software.
  • Knowledge of advanced Python features, especially async programming.
  • A strong product sense that manifests as a focus on developer ergonomics and productivity.
  • A high level of customer empathy, good communication skills, and an openness to working directly with our users to help solve their problems.
  • Ability to participate in on-call rotation and respond to production incidents.
  • Ability to work in-person in our NYC or Stockholm office.

Nice-to-Haves

  • Familiarity with modern data / ML / AI tools and workflows
  • Experience with Typescript, Go, or Rust
Apply Now

Modal

Member of Technical Staff - Systems

Full time • Stockholm

$140K – $200K • Offers Equity

The Role:

We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform.

Requirements

  • 5+ years of experience writing high-quality production code
  • Experience building high-performance distributed systems at a large scale (the more battle scars, the better)
  • Strong cloud skills
  • Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.)
  • Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!)
  • Ability to work in-person in our Stockholm office.
  • Prior experience with Rust is nice to have, but not required.
  • Ability to participate in on-call rotation and respond to production incidents.
Apply Now

Modal

Member of Technical Staff - Platform Engineering

Full time • New York; Stockholm

$150K – $350K • Offers Equity

The Role:

At Modal, we sell cloud services atop which our customers run their critical production systems. As a rapidly growing new cloud infrastructure company, we seek to improve our reliability dramatically while scaling the size of our platform, customer base, and our team. This role is for people who are deep systems thinkers, love stacking nines, and thrive from making others move faster at scale.

Responsibilities

  • Identifying architectural changes to improve reliability and performance.
  • Fostering a culture of reliability across Modal’s engineering organization.
  • Defining and implementing operational processes such as deployments, upgrades, etc.
  • Operating systems like Kubernetes, Postgres, Redis, etc.
  • Participating in on-call rotations, and responding to production incidents.

Requirements

  • 5+ years of experience writing high-quality production code.
  • 2+ years of on-call experience for critical production services.
  • Strong cloud skills, and deep familiarity with at least one hyperscaler cloud (AWS preferred).
  • Familiarity with auto scaling, fleet management, and capacity planning at scale.
  • Experience operating databases, monitoring, CI/CD, and other infrastructure, at scale
  • Experience owning and scaling Kubernetes clusters to thousands of nodes a plus.
  • Experience with systems safety research (e.g. STAMP) and control theory a plus.
  • Ability to work in-person in our NYC or Stockholm offices.
Apply Now

Modal

Systems Engineering Manager

Full time • Stockholm

$175K – $250K

The Role:

We’re looking for an Engineering Manager to lead a group of highly experienced engineers. This is a hands-on leadership role where you’ll spend roughly half your time on technical contribution and half on people management, depending on the need. You’ll work closely with the team to set direction, remove blockers, and foster a strong engineering culture as they tackle complex systems challenges in distributed computing, large-scale data handling, and performance optimization.

We think you are an experienced engineering leader who thrives close to the work and enjoys building alongside their team when needed. You earn trust through technical depth, communicate with clarity, and help great engineers move fast and make sound decisions. You thrive in a fast paced environment, you are pragmatic, calm under pressure, and focused on impact.

Requirements

  • At least 10 years of industry experience, including 3 years experience in a leadership role
  • Experience building high-performance distributed systems at a large scale
  • Strong background in cloud infrastructure
  • Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.)
  • Proficient in systems-level languages such as Rust, C, C++, or Java
Apply Now

Modal

Forward Deployed Engineer - ML

Full time • Stockholm

The Role:

We're looking for Forward Deployed ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you'll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads — LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You're helping teams reach outcomes most engineers can't on their own.

The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the AI stack, and energy for working directly with customers on hard problems.

Responsibilities

  • Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal
  • Contribute to open-source projects — members of the team are active contributors to SGLang — and publish technical content that demonstrates Modal's capabilities across the AI stack
  • Collaborate with Modal's product and sales teams, contributing to the platform as both an engineer and a product stakeholder
  • Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
  • Conduct technical demos, experiments, and proof-of-concepts that make Modal's performance advantages tangible

Requirements

  • 2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
  • Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go deep on at least one.
  • Strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership
  • Genuine interest in working directly with customers — you find it energizing to understand someone else's problem and help them solve it
  • Willing to work in-person in Stockholm

Nice-to-Haves

  • side projects, open-source contributions, or published work you're proud of in ML or systems performance
Apply Now