Technical Lead (Machine Learning)

Avomind; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Avomind is seeking a Technical Lead, Machine Learning to translate research into scalable, production-ready ML systems. This role sits at the intersection of research, infrastructure, and product, ensuring models are trainable, deployable, observable, and optimized for real-world performance.

You will partner with research, engineering, and product teams to deliver robust ML infrastructure and optimal inference solutions.

Qualifications

• Proven experience building and deploying production-grade ML systems.
• Strong understanding of large language models and failure modes.
• Experience developing scalable ML infrastructure and training pipelines.
• Solid software engineering with production-quality code.
• Ability to balance latency, reliability, scalability and cost.
• Independent ownership of technical initiatives from design to deployment.
• Excellent communication and cross-functional collaboration.

Responsibilities

• Lead end-to-end ML system development including data pipelines, training workflows, and deployment.
• Fine-tune and optimize models using modern techniques and distillation methods.
• Design scalable inference systems prioritizing latency, cost efficiency, and reliability.
• Develop data pipelines for synthetic and real-world data.
• Build evaluation frameworks for performance, safety and bias.
• Improve production deployments via GPU utilization and optimization.
• Collaborate with backend, desktop and mobile teams to integrate ML.
• Drive rapid iteration and data-driven optimization of systems.

Skills

Production ML systems

LLMs

ML infra

Software engineering

Latency & cost trade-offs

Ownership mindset

Cross-functional collaboration

Tools

Python

PyTorch

JAX

GPU training & inference

Job description

About the Company

Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team.

The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant.

Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive.

About the Role

Our client is seeking a Technical Lead, Machine Learning to lead the execution of its AI platform by translating research into scalable, production-ready machine learning systems. This role sits at the intersection of research, infrastructure, and product, with responsibility for ensuring models are trainable, deployable, observable, and optimized for real-world performance.

Working closely with research, engineering, and product teams, this position will drive the development of robust ML infrastructure while balancing performance, reliability, latency, and cost.

Key Responsibilities

• Lead the end-to-end execution of machine learning systems, including data pipelines, training workflows, evaluation frameworks, inference architecture, and production deployment.
• Fine-tune and optimize models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation.
• Design, build, and operate scalable inference systems with a focus on latency, cost efficiency, and reliability.
• Develop and maintain data pipelines for both synthetic and real-world training datasets.
• Build evaluation frameworks to measure model performance, robustness, safety, and bias in collaboration with research teams.
• Optimize production deployments through GPU utilization, memory efficiency, inference optimization, and scaling strategies.
• Partner closely with application engineering teams to integrate machine learning systems into backend, desktop, and mobile products.
• Continuously improve production systems through rapid iteration, monitoring, and data-driven optimization.

Requirements

• Proven experience building and deploying production-grade machine learning systems used by real users.
• Strong expertise working with large language models and understanding model behavior, limitations, and failure modes.
• Experience developing scalable ML infrastructure, training pipelines, and inference systems.
• Strong software engineering skills with the ability to write maintainable, production-quality code.
• Experience balancing real-world production constraints, including latency, reliability, scalability, cost, and safety.
• Strong ownership mindset with the ability to independently drive technical initiatives from design through deployment.
• Excellent communication and collaboration skills, with experience working in cross-functional, high-performing engineering teams.

Preferred Technical Skills

Experience with the following technologies is preferred:

• Python
• PyTorch and/or JAX
• GPU-based model training and inference systems