• Design, build, and operate the ingestion systems that process large volumes of multimodal data into usable, well-structured datasets • Own the ingestion path end to end, from how data lands to how it is validated, processed, tracked, and made available downstream • Build modality-specific processing steps for real-world source data, such as medical imaging processing, audio and video metadata extraction, quality validation, and notes processing • Build parsers, validators, and normalization logic that can systematically handle messy, non-standard, and high-variance source formats • Turn repeated one-off data handling work into reusable processing patterns, internal tooling, and platform capabilities • Build for high volume and high throughput, optimizing systems for reliability, cost, and speed • Work across distributed and parallel compute systems to process workloads that do not fit well on a single machine • Choose the right execution model for the workload, including batch processing, distributed execution, and modern compute patterns for unstructured data and inference-heavy processing • Diagnose and resolve bottlenecks across ingestion and processing systems, and keep performance from degrading as volume and modality complexity grow • Build validation and quality checks that catch bad, incomplete, or malformed data before it propagates downstream • Handle sensitive and regulated data, including PHI, with the security and care the domain demands, including de-identification where required • Track provenance, metadata, and usage constraints through the ingestion path so downstream use remains compliant and auditable • Raise the quality bar for observability, debuggability, and operational reliability across the ingestion layer • Partner with product and Data Lab to support new modalities, new partner requirements, and non-standard source data • Work directly with partner engineering teams when needed to translate source-system realities into robust ingestion and processing design • Surface recurring patterns that are worth standardizing into reusable transforms, validators, and internal tooling • Help shape how Protege handles new data types as the platform expands into more complex data environments

• Build polished product experiences for users using Resend. • Turn complex technical concepts into simple, clear, and trustworthy user experiences. • Own features end-to-end: product thinking, UX details, implementation, instrumentation, launch, and iteration. • Collaborate closely with design, engineering, and customer success to understand user problems and ship high-quality solutions. • Build debugging and observability experiences that help users understand what happened to their emails, webhooks, domains, and API requests. • Talk to users, understand customer pain, and turn those insights into better product decisions. • Help raise the bar for craft, performance, accessibility, reliability, and product quality across Resend.

• Contribute to the design, development, and maintenance of public-facing APIs used by enterprise customers. • Build and maintain integrations with external enterprise systems, including HRIS platforms. • Support shared platform services used across the engineering organisation. • Write clean, well-tested, and well-documented code, and participate in code reviews. • Work within large-scale multi-tenant cloud services, developing your understanding of scalability and fault-tolerance. • Use Generative AI tools and coding agents in day-to-day workflows to improve code quality and delivery speed. • Collaborate with teammates and cross-functional partners to solve problems and deliver customer value. • Contribute to production health, including on-call rotations and incident response, in a 'you build it, you run it' environment. • Identify and help resolve availability, reliability, and performance issues.

• Work with the operations team to build out a comprehensive training suite that enables FROs to do their best work assisting Waymo’s cars and riders