About the job
Applied AI, Machine Learning Engineer
Location | London (Hybrid)
Comp | Highly Competitive + Equity
Applied AI / Machine Learning Engineer — Singapore
Focus | Applied AI, Machine Learning, GenAI, Production ML Systems, End-to-End Deployment
We’re partnering with one of the most ambitious AI companies globally, building and deploying advanced AI systems to solve complex, high-impact problems for major organisations across the private and public sectors.
Backed by some of the most respected names in technology and venture capital, the company brings together engineers, researchers and operators from leading AI and technology organisations to build AI systems that move beyond demos and into real-world production.
Following significant growth internationally, they are now expanding their engineering team in Singapore and are looking for exceptional ML / Artificial Intelligence Engineers who can take complex problems, develop the right machine learning approach and turn that into scalable, production-ready systems.
This is an opportunity to work across the full AI lifecycle from problem definition and modelling through experimentation, engineering, deployment and iteration — while seeing your work directly influence large-scale organisations and real-world outcomes.
About The Role
As an Applied Machine Learning \ Artificial Intelligence Engineer, you’ll work on difficult, often ambiguous problems where machine learning and AI can materially improve the way organisations operate.
Rather than working on isolated models or narrow research problems, you’ll own problems end-to-end: understanding the underlying challenge, determining the appropriate modelling approach, building and evaluating solutions, and deploying them into production environments.
The work can span classical machine learning, deep learning, generative AI and LLM-based systems depending on the problem being solved.
You’ll work closely with other artificial intelligence engineers, technical leaders and end users to ensure the systems you build are technically strong, practical and capable of delivering measurable impact.
Examples Of The Types Of Problems You Might Tackle
- Building ML systems that predict, classify, optimise or automate complex operational decisions
- Developing intelligent workflows combining structured data, unstructured data and language models
- Designing and evaluating models for high-stakes real-world environments
- Building LLM and agent-based systems where generative AI is the right technical solution
- Developing data pipelines, APIs and supporting infrastructure required to productionise AI systems
- Improving model accuracy, reliability, latency and scalability in production
- Taking an ambiguous business or operational problem and translating it into a well-defined machine learning problem
- Building systems that continuously improve as new data and feedback become available
What You’ll Do
As an artificial intelligence engineer, you will:
Build production AI systems
Design, develop and deploy machine learning and AI systems that solve complex real-world problems.
Own the full ML lifecycle
Work across problem formulation, data exploration, feature engineering, modelling, experimentation, evaluation, deployment and monitoring.
Choose the right technical approach
Apply classical ML, deep learning, LLMs or hybrid approaches depending on what the problem actually requires rather than defaulting to a single technology.
Engineer for production
Build reliable software, data pipelines, APIs and infrastructure around your models so solutions can operate effectively at scale.
Work closely with end users
Understand how organisations operate, identify where AI can create genuine leverage and rapidly iterate based on real-world feedback.
Solve ambiguous problems
Turn loosely defined, high-level challenges into measurable technical problems and practical solutions.
Shape the engineering culture
Contribute to technical decisions, engineering standards and the way AI systems are built and deployed across the organisation.
What We’re Looking For
- Strong professional experience across Machine Learning, Applied AI or ML Engineering
- Excellent coding ability, particularly in Python
- Strong software engineering fundamentals and experience building production systems
- Strong understanding of core machine learning concepts
- Experience taking machine learning models from experimentation into production
- Ability to work comfortably with complex or imperfect real-world datasets
- Strong understanding of system design and the engineering required around ML systems
- Experience independently owning technically challenging projects
- Strong communication skills and the ability to explain technical decisions clearly
You’ll Likely Thrive Here If
- You enjoy solving difficult problems rather than simply implementing predefined requirements
- Experience building applications using LLMs, foundation models or generative AI
- You’re equally comfortable thinking about models, data and software engineering
- You care about whether an AI system actually works in production, not just whether the model performs well in a notebook
- You naturally think about evaluation, failure modes and how to improve systems over time
- You’re comfortable operating with ambiguity and significant ownership
- You like working directly with the people ultimately using the systems you build
- You want to see a clear connection between your engineering work and tangible real-world outcomes
- You’re excited by both traditional machine learning and the opportunities emerging from generative AI
Nice To Have
- Experience with RAG, agents, tool use, or structured LLM workflows
- Experience fine-tuning or adapting open-source models
- Experience with deep learning frameworks such as PyTorch
- Experience designing ML evaluation frameworks and experimentation systems
- Experience with cloud infrastructure and production ML platforms
- Experience working in high-growth technology companies, research environments or technically demanding engineering teams
- Strong academic background in Computer Science, Machine Learning, Mathematics, Statistics or a related discipline
What’s On Offer
- Highly competitive compensation
- Meaningful equity upside
- Opportunity to work alongside exceptional AI engineers, researchers and operators
- Significant technical ownership from day one
- Exposure to some of the most interesting and consequential applied AI problems being tackled globally
- The ability to see systems you build deployed into real organisations and create measurable impact
- A fast-moving environment where strong engineers can have outsized influence on both the technology and the company
Interested in this Role?
If you’re interested in learning more, feel free to reach out | danny@salientgroup.com.au
