This is a highly collaborative role. You'll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers.

As a researcher in this role, you'll be expected to:

• Own research projects on audio training, low-latency inference, and conversational responsiveness.
• Design and train large-scale models that natively support audio input and output.
• Investigate scaling behaviour such as how data, model size, and compute affect capability and efficiency.
• Build and maintain audio data pipelines, including preprocessing, filtering, segmentation, and alignment for training and evaluation.
• Collaborate with data and infrastructure teams to scale audio training efficiently across distributed systems.
• Publish and present research that moves the entire community forward.

Share code, datasets, and insights that accelerate progress across industry and academia.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports.

It's an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

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Day-to-day, you'll work across the stack - from React-based frontends to backend services running on Kubernetes - while integrating AI/LLM capabilities into real-world applications. This role offers high visibility and the opportunity to directly influence how data is consumed and operationalized across the company.

The ideal candidate has 7+ years of experience building production-grade software applications, including both backend services and modern web frontends. They should have strong proficiency in backend programming languages (Python, Go, Java, C#) and frontend programming languages (JavaScript, TypeScript).

In addition to technical skills, the successful candidate will be a curious and creative problem-solver who is passionate about building user-facing applications that turn complex data into intuitive experiences. They should be able to take ownership of complex systems end-to-end, from design through deployment and iteration.

At CoreWeave, we work hard, have fun, and move fast! We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

itertools[''] -> Be Curious at Your Core itertools[''] -> Act Like an Owner itertools[''] -> Empower Employees itertools[''] -> Deliver Best-in-Class Client Experiences itertools[''] -> Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems.

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Your work will directly shape what users experience: designing APIs that serve rich content, building services that handle real-time interactions, implementing content-matching systems for rights and safety, and ensuring our platform performs under load. You'll architect systems that are fast, correct, and maintainable.

You'll collaborate closely with Product, ML Research, and Mobile/Web teams to ship features that matter. We use Python, Go, BigQuery, Pub/Sub, and a microservices architecture,but we care more about good judgment than specific tool experience.

Responsibilities

• Design and maintain application-level data models that organize rich content into canonical structures optimized for product features, search, and retrieval.
• Build high-reliability ETLs and streaming pipelines to process usage events, analytics data, behavioral signals, and application logs.
• Develop data services that expose unified content to the application, such as metadata access APIs, indexing workflows, and retrieval-ready representations.
• Implement and refine fingerprinting pipelines used for deduplication, rights attribution, safety checks, and provenance validation.
• Own data consistency between ingestion systems, application surfaces, metadata storage, and downstream reporting environments.
• Define and track key operational metrics, including latency, completeness, accuracy, and event health.
• Collaborate with Product teams to ensure content structures and APIs support evolving features and high-quality user experiences.
• Partner with Analytics and Research teams to deliver clean usage datasets for experimentation, model evaluation, reporting, and internal insights.
• Operate large analytical workloads in BigQuery and build reusable Dataflow/Beam components for structured processing.
• Improve reliability and scale by designing robust schema evolution strategies, idempotent pipelines, and well-instrumented operational flows.

Requirements

• Experience building production backend services and APIs at scale
• Experience building ETL/ELT pipelines, event processing systems, and structured data models for applications or analytics
• Strong background in data modeling, metadata systems, indexing, or building canonical representations for heterogeneous content
• Proficiency in Python, Go, SQL, and scalable data-processing frameworks (Dataflow/Beam, Spark, or similar)
• Familiarity with BigQuery or other analytical data warehouses and strong comfort optimizing large queries and schemas
• Experience with event-driven architectures, Pub/Sub, or Kafka-like systems
• Strong understanding of data quality, schema evolution, lineage, and operational reliability
• Ability to design pipelines that balance cost, latency, correctness, and scale
• Clear communication skills and an ability to collaborate closely with Product, Research, and Analytics stakeholders

Nice to Have

• Experience building application-facing APIs or microservices that expose structured content
• Background in information retrieval, indexing systems, or search infrastructure
• Experience with fingerprinting, perceptual hashing, audio similarity metrics, or content-matching algorithms
• Familiarity with ML workflows and how downstream analytics and usage data feed back into research pipelines
• Understanding of batch + streaming architectures and how to blend them effectively
• Experience with Go, Next.js, or React Native for occasional full-stack contributions

Why Join Us

You will design the core data services and pipelines that power our product experience, analytics, and business operations. You’ll work on high-impact data challenges involving real-time signals, large-scale metadata systems, and cross-platform consistency. You’ll join a small, fast-moving team where you’ll shape the structure, reliability, and intelligence of our downstream data ecosystem.

Benefits

• Highly competitive salary and equity
• Quarterly productivity budget
• Flexible time off
• Fantastic office location in Manhattan
• Productivity package, including ChatGPT Plus, Claude Code, and Copilot
• Top-notch private health, dental, and vision insurance for you and your dependents
• 401(k) plan options with employer matching
• Concierge medical/primary care through One Medical and Rightway
• Mental health support from Spring Health
• Personalized life insurance, travel assistance, and many other perks

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