Senior Fullstack Software Engineer
Build systems that integrate with the EHRs used in American healthcare to make Heidi feel like a native capability rather than a plugin. Develop systems that simplify the complexity of US healthcare billing, compliance, and payer constraints so clinicians do not have to manage these complexities. Write clean, testable code with strong interfaces, error handling, and observability, ensuring the workflows are reliable for clinicians, operators, and downstream systems. Focus on outcomes by ensuring that the built systems help clinicians and improve practice revenue. Create agentic workflow functionalities where AI assists with extraction, reconciliation, and drafting within workflows, incorporating human review, auditability, and control. Collaborate closely in a team environment with frequent pairing and shared ownership of design and implementation. Learn about healthcare organizational operations, especially those serving US customers, to translate requirements and constraints into product improvements.
Software Engineer, Backend
Design, build, and own backend systems end-to-end, including services, APIs, data pipelines, and infrastructure that power the products. Solve complex technical challenges across distributed systems, scaling, concurrency, and performance. Integrate and operate large generative AI models in production by deploying, serving, and scaling systems that combine internal research and external capabilities to unlock new product experiences. Instrument, experiment, and iterate in production to continuously improve system and product quality. Design and operate core platform infrastructure, including integrations with third-party providers, storage systems, security, and internal APIs.
Field Events Marketing Manager
Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.
Hardware Tools Engineer
Develop and evolve the tooling ecosystem that hardware engineers rely on, including hardware compilers, IR transformations, simulation, debugging, and automation infrastructure. Build and improve software tooling to enhance hardware teams' efficiency, including compilation, IR transforms, RTL generation, simulation, debugging, and automation. Extend and integrate hardware compiler stacks (frontends, IR passes, lowering, scheduling, code generation to Verilog/SystemVerilog) and connect them to real design workflows. Improve developer experience and reliability by enabling reproducible builds, better error messages, faster iteration loops, and dependable continuous integration and regression infrastructure. Collaborate closely with architects, RTL designers, and verification engineers to translate engineering friction points into durable, scalable tooling solutions. Read and reason about Verilog/SystemVerilog to debug issues, validate tool output, and improve tool debuggability. Engage with detailed hardware levels including gate-level views, synthesis results, and implementation artifacts when necessary. Facilitate PPA optimization loops by developing analysis and automation tools addressing area, timing, and power trade-offs, and improve tooling impacting those outcomes.
Software Engineer (AI)
Work closely with the Product Lead as a Mid-level or Senior Fullstack Engineer operating at the intersection of core product development and clinical application, building end-to-end AI features including architecting and shipping fullstack solutions from React frontends to Python backend services that leverage voice AI and LLMs to automate clinical workflows. Implement and fine-tune audio processing pipelines to ensure Automatic Speech Recognition (ASR) and LLM agents perform accurately in diverse, real-world medical environments. Translate complex clinician feedback into technical solutions, rapidly prototyping and deploying improvements to model behavior, prompting strategies, and audio handling. Optimize fullstack performance to handle real-time audio streaming and token generation to minimize latency and create seamless conversational experiences for clinicians. Partner with implementation and clinical teams to quickly ship critical integrations and feature requests from concept to production in days rather than quarters.
Senior Software Engineer
Build systems that integrate seamlessly with the EHRs used in American healthcare to feel like a native capability rather than a plugin. Develop systems that handle complex US healthcare billing rules, compliance requirements, and payer constraints so that clinicians do not have to manage these complexities. Write clean, testable code with strong interfaces, error handling, and observability for workflows depended on by clinicians and operators. Focus on outcomes that help clinicians and improve practice revenue, not just code functionality. Create AI-assisted workflow functionality that supports extraction, reconciliation, and drafting with human review, auditability, and clear controls. Collaborate closely with others through frequent pairing and shared design and implementation ownership. Learn about healthcare organizations' practical operations and US customer requirements to guide product improvements.
Software Engineer, Agent
Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth. Own and manage the Agent Development Life Cycle (ADLC) with complete autonomy from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments. Partner with large enterprises and startups to understand business challenges and build AI agents that transform their operations at scale. Build the future of Sierra's core platform by surfacing unmet customer needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and Sierra's product.
Member of Technical Staff, Pre-training Systems
As a Software Engineer on the Pre-training Systems team, you will design and operate the distributed infrastructure that trains Magic's long-context models at scale. You will focus on large-scale model training across massive GPU clusters, working at the boundary between deep learning and distributed systems to ensure training runs are performant, reliable, and reproducible under extreme scale. Your responsibilities include scaling distributed training across large GPU clusters using data, tensor, and pipeline parallelism; optimizing communication patterns and gradient synchronization; improving checkpointing, fault tolerance, and job recovery systems; profiling and eliminating performance bottlenecks across compute, networking, and storage; improving experiment reproducibility and orchestration workflows; increasing hardware utilization and training throughput; and collaborating with Kernels and Research to align model architecture with systems realities.
Member of Technical Staff, Inference & RL Systems
As a Software Engineer on the Inference & RL Systems team, you will design and operate the distributed systems that serve models in production and power large-scale post-training workflows. Responsibilities include designing and scaling high-performance inference serving systems, optimizing KV-cache management, batching strategies, and scheduling, improving throughput and latency for long-context workloads, building and maintaining distributed RL and post-training infrastructure, improving the reliability of rollout, evaluation, and reward pipelines, automating fault detection and recovery for serving and RL systems, profiling and eliminating performance bottlenecks across GPU, networking, and storage layers, and collaborating with Kernels and Research teams to align execution systems with model architecture.
Software Engineer
As a Software Engineer at Magic, you will work on core systems or product surfaces that directly determine model capability and user experience. The role includes end-to-end ownership: defining problems, implementing solutions, shipping to production, and iterating based on real outcomes. Responsibilities may include building and scaling large distributed data pipelines for pre-training; designing filtering, mixture, and dataset versioning systems; developing post-training datasets, evaluation frameworks, and reward pipelines; running ablations that translate capability goals into measurable improvements; building end-to-end product surfaces that integrate deeply with the model; designing APIs, backend services, and frontend workflows for AI-first experiences; and improving reliability, observability, and performance of production systems. The position involves working with the unique technical challenges posed by long-context models such as internet-scale data acquisition, long-horizon post-training loops, and product workflows that make complex model behavior understandable and controllable. The role can evolve into specialization in data systems, post-training capability development, or product engineering leadership, depending on strengths and interests.
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