Time since we first saw it
Day 8
Similar jobs usually come down between day 7 and day 71.
From 4,682 similar postings that came down. Coming down is not always a hire.
Ho Chi Minh
Actively hiring, first seen by us 8 days agoWork setup in the employer’s posting: remoteFrom the employer’s posting
Employment type Full-time
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Build and maintain ML pipelines on Databricks and AWS with monitoring and retraining
Build agentic workflows that combine models, tools and data for multi-step tasks
Optimize model training and inference for latency and cost
Set up offline evals, online A/B tests and quality guardrails for AI systems
Redesign engineering and business workflows with AI tools and measure the impact
Develop and maintain scalable ML pipelines on Databricks and AWS covering data ingestion, feature engineering, model training, evaluation and serving
Build systems that combine models, tools and data into cohesive, agentic workflows capable of executing multi-step tasks
Identify and resolve performance bottlenecks in model training and inference through profiling, optimization, and architectural improvements
Set up evaluation frameworks for LLM and ML systems, including offline evals, online A/B tests and quality guardrails
Find and redesign engineering and business workflows using AI tools, and measure the impact
Actively hiringFirst seen by us 8 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 8
Similar jobs usually come down between day 7 and day 71.
From 4,682 similar postings that came down. Coming down is not always a hire.
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Tech Stack: - Languages: Python (primary) - Data & ML platform: Databricks (Spark, MLflow, Unity Catalog), AWS (SageMaker, S3, Lambda, ECS/EKS)
- Experience using Cursor, Copilot, Codex or similar AI coding assistants for development, debugging, testing and refactoring. - Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL and data exploration, and speeding up engineering workflows. - Has integrated AI tools to improve engineering efficiency and code quality, or to redesign workflows, with measurable impact such as time saved, defect reduction or throughput gains.
what you'll do - Develop and maintain scalable ML pipelines on Databricks and AWS covering data ingestion, feature engineering, model training, evaluation and serving, with monitoring, drift detection and automated retraining. - Build systems that combine models, tools and data into cohesive, agentic workflows capable of executing multi-step tasks. This includes designing system behaviors such as planning, tool use, structured outputs and failure handling.
- Languages: Python (primary) - Data & ML platform: Databricks (Spark, MLflow, Unity Catalog), AWS (SageMaker, S3, Lambda, ECS/EKS) - Vector search / RAG: Qdrant
- Frameworks: PyTorch, Hugging Face; LLM APIs (Anthropic, OpenAI) and agent frameworks - Nice to have: LLM observability and eval tooling (e.g. Datadog LLM Observability), Docker/Kubernetes, CI/CD for ML life at clearer.io
- Inference optimisation: TensorRT - Frameworks: PyTorch, Hugging Face; LLM APIs (Anthropic, OpenAI) and agent frameworks - Nice to have: LLM observability and eval tooling (e.g. Datadog LLM Observability), Docker/Kubernetes, CI/CD for ML
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New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Engineering has changed. We're changing with it. At clearer.io, we build the software that helps eCommerce merchants win on search, discovery, and customer engagement. Building that well, in this market, means the engineering behind it can't stand still either. AI has moved past being a coding aid, it's reshaping how software actually gets built, from spec to delivery, and we're moving with it rather than waiting for the dust to settle: agentic workflows, new tooling, new ideas about what a software factory even looks like. We build on and around Shopify, a platform that changes just as fast, so staying current isn't a nice to have here, it's the job.
That's why we're not hiring for where engineering was. We're hiring the next level of engineer, people who see this pace of change as the interesting part of the work, not something to survive until things settle down.
Your Impact:
As a Senior AI/ML Engineer, you will design, build and run production AI/ML systems, from classic ML models to LLM and agent-based applications, that improve the user experience and business results. You will lead technical work on large AI systems, set a high bar for AI-native engineering practice, mentor engineers and work with stakeholders to define and deliver AI initiatives.
Develop and maintain scalable ML pipelines on Databricks and AWS covering data ingestion, feature engineering, model training, evaluation and serving, with monitoring, drift detection and automated retraining.
Build systems that combine models, tools and data into cohesive, agentic workflows capable of executing multi-step tasks. This includes designing system behaviors such as planning, tool use, structured outputs and failure handling.
Identify and resolve performance bottlenecks in model training and inference through profiling, optimization, and architectural improvements, optimizing for latency and cost (e.g., TensorRT, quantization, batching).
Set up evaluation frameworks for LLM and ML systems, including offline evals, online A/B tests and quality guardrails, and ensure model fairness, bias mitigation and responsible AI practices.
Find and redesign engineering and business workflows using AI tools, and measure the impact.
Collaborate with cross-functional teams to translate product requirements into production-ready systems, and continuously improve system performance through iteration and evaluation.
Mentor engineers on ML practice and AI-assisted development.
Proven track record of deploying AI/ML solutions at scale in production.
Experience in one or more specialized ML domains: search, recommendation systems, natural language processing (NLP), computer vision or generative AI.
Experience leading and owning end-to-end delivery of AI/ML projects, including technical design, cross-functional collaboration, and operational readiness.
Experience using Cursor, Copilot, Codex or similar AI coding assistants for development, debugging, testing and refactoring.
Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL and data exploration, and speeding up engineering workflows.
Has integrated AI tools to improve engineering efficiency and code quality, or to redesign workflows, with measurable impact such as time saved, defect reduction or throughput gains.
Keeps building AI skills, for example prompt and context engineering or agent orchestration, and stays current with new AI technologies.
Excellent communication and mentorship skills.
Tech Stack:
Languages: Python (primary)
Data & ML platform: Databricks (Spark, MLflow, Unity Catalog), AWS (SageMaker, S3, Lambda, ECS/EKS)
Vector search / RAG: Qdrant
Inference optimisation: TensorRT
Frameworks: PyTorch, Hugging Face; LLM APIs (Anthropic, OpenAI) and agent frameworks
Nice to have: LLM observability and eval tooling (e.g. Datadog LLM Observability), Docker/Kubernetes, CI/CD for ML
eCommerce is crowded, noisy and increasingly complex. At clearer.io, we're built for a different outcome: clarity you can act on.
We connect to the stack a merchant already uses. Our AI agents enrich their catalogue, learn from their reviews and customers, and put that intelligence to work inside the tools they already own — so their products get found, trusted and chosen by shoppers and AI, without replatforming. We do this across our products: Clearer, Boost Commerce, REVIEWS.io and Influence, used by thousands of merchants worldwide.
We're a global team with offices in Australia, the UK, Germany, Vietnam and the US, backed for the long term by PSG Equity and Silversmith Capital Partners.
We care about doing the work properly: listening first, building second and shipping quickly where it matters. Results matter more than rhetoric.
What guides us day to day:
Customers at the heart: we obsess over their needs so we can grow together
Purposeful progress: we don't settle - we take initiative and embrace bold thinking
Endless innovation: we refine, improve, and take meaningful action
Always open: we build trust through transparency and global collaboration
Everyone, everywhere
Your birthday off - a full paid day, just for you, every year
Give Back Day - a paid day each year to volunteer for a cause you care about
Bonusly peer recognition - real-time shout-outs tied to our values, with points that convert to real rewards
Clearer Together - employees who exemplifies our values wins a fully-funded 5-night trip to one of our global offices (Sydney, Berlin, Leicester, Irvine or Ho Chi Minh City)
Stopover - travelling near one of our offices? Once a year we'll fund a day with the local team
VN
100% Remote Culture: Work from anywhere in Vietnam that brings you happiness.
Performance-based Year-End Bonus: Recognizing and rewarding your individual contributions.
Wellness Allowance: Support for classes promoting physical and mental health.
Time Off: 20 days/year, in addition to Vietnam holidays.
MacBook Provided.
Collaborative Events: Offline meet-ups, monthly gatherings and year-end party.
Continuous Learning: Technical and general workshops, online resources.
Health and Wellness Benefits: Generali Health Care, annual check-up.
International Exposure: Enhance expertise and English communication skills.
a fair shot for everyone
We're committed to a fair and inclusive hiring experience. Hiring decisions at clearer.io are based on skills, experience and role requirements - nothing else. If you need any adjustments during the interview process, just tell us; it won't affect your application.
Full-time
Selected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
Ho Chi Minh
VN - 100% Remote Culture: Work from anywhere in Vietnam that brings you happiness. - Performance-based Year-End Bonus: Recognizing and rewarding your individual contributions.
No clear work-authorization passage found. Eligibility is unconfirmed.