Responsibilities

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 Tech Lead 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.

Job Information