About the job
As a Senior Software Engineer, you will own full-stack features in our workflow related services. You’ll work closely with product managers, designers, and AI/ML engineers to turn ambiguous creator and platform needs into reliable tools that make shipping AI-powered effects project fast, safe, and enjoyable.
Responsibilities
- Own end-to-end development of key features in our workflow editor and related services – from problem discovery and design, to implementation, rollout, and long-term maintenance.
- Design and build full-stack product experiences for the editor, including rich graph-based UI (React/TypeScript or similar) and backend services (Go or equivalent) that orchestrate workflow execution, scheduling, and state management.
- Collaborate with AI/ML platform teams to integrate models and pipelines (e.g., LLMs, diffusion, vision models) into reusable workflow nodes, with clear contracts, versioning, and observability.
- Build internal tooling for debugging and operating workflows: run history, logs & metrics, replay, tracing, and guardrail/safety controls surfaced in the editor.
- Partner with product and data/ML teams to design experiments and evaluation flows around workflows (e.g., offline metrics, staged rollout, A/B experiments) and fold the results back into product decisions.
- Improve the reliability, performance, and usability of the editor and backend services through systematic instrumentation, alerting, on-call participation, and regular post-mortems.
- Work closely with designers to refine interaction patterns for complex graph editing, parameter configuration, and status visualization, balancing power and simplicity.
- Contribute to the overall roadmap of the workflow platform, identifying reusable abstractions and collaborating with other teams that depend on workflow capabilities.
Qualifications
Minimum
- Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience with 5+ years of professional software engineering experience building production systems end-to-end; at least 2 years working on AI/ML-powered or data-intensive products, platforms, or tools.
- Strong software engineering fundamentals: data structures & algorithms, system design, testing, and code quality.
- Solid backend experience in Go or another typed language (e.g., Java/Scala/C++), including designing and operating APIs/services, working with relational and/or NoSQL databases, and building reliable distributed systems.
- Hands-on experience with modern web front-end technologies (TypeScript/JavaScript, React or similar frameworks), including building complex interactive UIs and integrating them with backend APIs.
- Practical experience integrating ML/LLM capabilities into products or tools: calling model APIs, handling latency/reliability trade-offs, and working with logs/metrics for quality monitoring.
- Experience working closely with product managers, designers, and ML/data partners to define requirements and iterate quickly based on user feedback.
- Strong communication skills, with the ability to explain technical trade-offs and AI behavior to non-specialists.
Preferred
- Experience building workflow editors or node-based tools (e.g., low-code editors, DAG/workflow UIs, graph-based editors, or tools built with React Flow or similar libraries).
- Experience with ML/LLM workflow and orchestration tools (e.g., Airflow/Prefect/Dagster on the data side; LangChain, LlamaIndex, Semantic Kernel, or equivalent on the application/agent side).
- Experience building internal platforms or tools for ML/AI teams, such as evaluation platforms, prompt/agent sandboxes, or observability tools for AI systems.
- Familiarity with applied LLM patterns such as RAG, tool-calling, multi-step agents, and evaluation techniques (including offline metrics and LLM-as-a-judge-style workflows).
- Experience working with AIGC pipelines (e.g., image/video generation, compositing, or effect processing) or creative tooling is a strong plus.
- Experience operating services in a cloud environment (e.g., Kubernetes-based deployments, monitoring/alerting stacks, on-call ownership).
- Prior 0→1 or early-stage builder experience (e.g., startup, internal incubation team, or founding engineer roles) with a track record of shipping ambiguous projects.