About the team

Our team supports platform behind TikTok’s AIGC and effects submission pipelines. We develop and operate the tools and workflow runtime that internal creators, effect designers, and AI/ML teams use to compose, monitor, and debug complex AI workflows — from model inference chains to asset processing and publishing.
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 Qualification(s):
- 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 Qualification(s):

• 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.