Group Information
Carousell Group is the leading recommerce group in Greater Southeast Asia on a mission to inspire the world to start selling, and to make secondhand the first choice. Founded in August 2012 in Singapore, the Group has a leading presence in eight markets under the brands Carousell, Cho Tot, Laku6, Mudah.my, OneKyat, Ox Street, and Refash, serving tens of millions of monthly active users. Carousell is backed by leading investors including Telenor Group, Rakuten Ventures, Naver, STIC Investments and Sequoia Capital India. 

As a team of passionate individuals working together to solve meaningful problems, there is so much more for you to discover in a career with Carousell. Our culture is made up of hiring, developing, and promoting people who embody our values of solving problems for our users; having a mission-first mindset; being relentlessly resourceful; caring deeply; and staying humble to constantly improve. Together as an organisation, we make magic happen.

Company Information
• Chợ Tốt is part of Carousell Group, the fastest-growing classifieds technology group in Southeast Asia. Since launching in the Vietnamese market in 2013, Chợ Tốt has become a recommerce platform for buying and selling second-hand goods, with more than 10 million users and over 1 million items listed for resale every month. As Vietnam’s leading online classifieds site, we are passionate about making life more convenient for everyone.

• Our mission is to help everyone find a way to any of their wants (“Muốn là có”). We are a dynamic family with young and energetic members seeking motivated talents to join our fast-moving and fun-loving crew..

• For more information please visit https://careers.chotot.com/about-us/

• Translate ambiguous business problems into well-defined ML/DS problems. Investigate the real issue before proposing a solution, scope the right approach, and know when ML is the wrong answer.
• Set technical direction for ML initiatives in your problem area. Make build-vs-buy, ML-vs-heuristic, and model-architecture decisions based on cost, latency, complexity, and business impact — not novelty.
• Own ML systems end-to-end in production: data ingestion, training, serving, monitoring, retraining, and rollback. Anticipate and design around failure modes — data drift, train/serve skew, feedback loops, cold start, and label leakage — before they hit users.
• Build and ship the services, APIs, and integrations around your models, working across the stack to deliver ML-powered features users actually touch.
• Define the metrics that matter — offline evaluation, online A/B tests, and long-term business KPIs — and defend them. Question metrics you're handed when they don't reflect real user or business value.
• Partner with product, design, and business stakeholders to shape the roadmap, not just execute it. Translate model behavior and data insights into narratives that drive decisions at both tactical and executive levels.
• Raise the technical bar of the team through design reviews, code reviews, and mentorship. Your output is measured by what you ship and by what you unblock and elevate in others.

• Total 5+ years of experience designing, building, and operating production — ideally in e-commerce or marketplace domains (recommendation, search, ranking, pricing, segmentation, or trust & safety).
• At least 2 YOE working as ML/AI engineer role
• Fluent use of AI coding assistants and LLM-based tools in your daily workflow (Claude Code, Copilot, or equivalent). At Chotot, AI is a compulsory tool in product development.
• Hands-on experience owning ML systems end-to-end in production environments
• Excellent English communication — you will work directly with cross-country teams
• Experience building and scaling ML-powered products from early-stage development to large-scale production usage, with strong ownership across product iteration, system reliability, and business impact.
• Track record of ML systems you personally took from problem framing to production and measurable business impact — not just trained models, but shipped features users interacted with.
• Experience Python and SQL, with hands-on experience in a cloud data warehouse (BigQuery preferred) and a public cloud (GCP preferred).
• Solid grasp of both classical ML and deep learning. You pick the right tool for the constraint rather than defaulting to the latest paper.

Nice-to-have

• Experience with vector databases and embedding-based retrieval.
• Familiarity with event-driven and streaming data pipelines.
• Prior experience mentoring or setting technical direction.

Thank you for taking your time to read our job description and thank you in advance if you decide to apply for this position. Shortlisted candidates will be contacted within 2 weeks since application, otherwise we might meet when another chance arises.

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