Tech Lead, Machine Learning Engineer - Global E-Commerce (Conversational AI)

ByteDance; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

ByteDance seeks a Tech Lead, Machine Learning Engineer to drive a global E‑commerce conversational AI platform. You will own roadmaps across post‑training, agent harness, and evaluation, and mentor 1–3 ICs to raise the team's technical bar.

You’ll shape core abstractions, write design docs, and collaborate with model, infra, and product teams to deliver scalable, multilingual agents across 30+ languages for buyers and sellers.

Qualifications

• BS / MS / PhD in CS, AI or related quantitative field.
• Hands‑on ML/NLP/DL experience; post‑training or fine‑tuning of frontier‑class LLMs.
• Strong Python and production‑path code in C++/Go/Rust.
• Led at least one production LLM/Agent system.

Responsibilities

• Set technical direction and multi‑quarter roadmaps for post‑training, agent harness, and evaluation.
• Hire and mentor 1‑3 ICs; raise technical bar through design/code reviews.
• Write core abstractions and design docs; own cross‑team alignment on decisions.
• Drive observability, rollback tooling, and performance improvements across the stack.

Skills

Python

C++

Go

Rust

ML/NLP/Applied DL

Production LLMs / Agent systems

Leadership & mentoring

Post-training / fine-tuning

Education

BS / MS / PhD in CS, AI or related quantitative field

Tools

PyTorch

TensorFlow

Job description

Tech Lead, Machine Learning Engineer - Global E-Commerce (Conversational AI)

Overview

We are building the next generation of conversational AI for Global E-commerce — a unified Agent system that learns from every interaction, runs in 30+ languages, and is deployed across one of the largest e-commerce surfaces on the internet. Our 2026 north star is a self‑evolving Agent: post‑training, harness, tools, memory, and evaluation form one closed loop, and every served conversation becomes training, evaluation, and retrieval signal for the next iteration. Business surface — buyer, seller, dispute & appeals, operations — is the substrate. Our work is foundational LLM + Agent engineering: post‑training, agent harness, tool design, memory, evaluation, inference, multilinguality. We are hiring people who want to push the SOTA of these systems in production, at scale, with hundreds of millions of users in the loop.

Responsibilities

Set technical direction. Own a multi‑quarter roadmap across one or more of: post‑training, agent harness, evaluation, self‑evolving data flywheel, serving. Translate north‑star metrics into a sequence of 2–3 high‑ROI bets per quarter and ship them.

Compound the team. Hire and develop 1‑3 strong ICs. Design their work surfaces for growth, not just dispatch. Raise the median technical bar through design review, code review, and 1:1 framing.

Stay in the loop with the model. Tech Lead is not a manager role. You still write the load‑bearing PRs, propose the core abstractions, and write the design docs that decide the team's ceiling for the next 2‑3 quarters.

Drive cross‑team alignment. Partner with foundation‑model, infra, product, and adjacent algorithm teams; own sign‑off on cross‑cutting technical decisions.

Observability and rollback. Build the per‑turn tracing, tool‑call analytics, and failure‑mode taxonomies that let the team diagnose any regression within hours, not days.

Qualifications

Minimum Qualifications

• BS / MS / PhD in CS, AI, Mathematics, or related quantitative field.
• Hands‑on experience in ML / NLP / applied DL. Top PhDs with strong publication record may qualify at 4+ years.
• Strong Python and at least one of C++ / Go / Rust for production‑path code.
• Hands‑on post‑training or fine‑tuning of frontier‑class LLMs (≥7B, multi‑node). Not API‑only.
• Has led at least one production LLM / Agent system from zero to one.

Preferred Qualifications

• LLM post‑training — multi‑node SFT / DPO / online RL on ≥7B models; reward modeling; preference data construction; RLAIF / RLVR; distillation; QAT; long‑context training; continual pre‑training.
• Agent engineering (Anthropic‑style) — production agent harness, context engineering, sub‑agents, durable execution, MCP / Skill‑style extensibility, parallel tool use, computer use.
• Reasoning & planning — chain‑of‑thought / reasoning‑trace training, planner/critic decomposition, self‑consistency, verifier models, multi‑step reasoning evaluation.
• LLM / Agent evaluation — LLM‑as‑judge with human‑agreement calibration, tau‑bench / SWE‑bench / GAIA / BFCL‑style harnesses, regression + safety + cost + latency‑aware evaluation.
• Inference & serving systems — vLLM / TensorRT‑LLM, MoE, speculative decoding, KV‑cache and prompt caching, low‑latency multi‑tenant serving.
• Multilingual & cross‑cultural reasoning — multilingual SFT/DPO, low‑resource adaptation, faithful MT, locale‑aware reasoning.