Lead Engineer / Tech Lead, Machine Learning

Axe Recruiting; United States; Hybrid; USD 180,000 - 300,000; Full time

Job summary

Axe Recruiting represents a well-funded AI applications company seeking an ML Lead to own the full model lifecycle and production ML systems. You will work at the interface of research, systems and product, shipping production-grade models and enabling real users to benefit from advanced AI applications.

The role requires hands-on leadership across data, training, evaluation, inference and deployment, with GPUs, PyTorch and JAX, in a hybrid setup across the US/UK/Seoul.

Qualifications

• Shipped ML systems in production with real users.
• Strong understanding of modern large models: training, fine-tuning, evaluation, serving.
• Experience running ML workloads at scale on GPU clusters.
• Production-grade code quality and reliability.

Responsibilities

• Take charge of ML systems across data, training, evaluation, inference and deployment.
• Develop pipelines to train and fine-tune large models and continuously improve them.
• Build evaluation metrics for capability, safety, robustness and user impact.
• Architect fast inference with attention to GPU utilization, memory, latency and cost.
• Create data systems providing high-quality training data, real and synthetic.

Skills

ML systems

Production-grade code

GPUs

PyTorch

JAX

System design

Tools

PyTorch

JAX

Job description

Own the execution side of the intelligence: make research and model capability run as ML systems that hold up at scale.

The Opportunity

Our client is looking for the ML lead who makes large models behave in production. A well-funded AI applications company, backed by an established technology company in Southeast Asia and building for a global market, they are building AI-native versions of everyday productivity software. This role owns the full model lifecycle: data, training, evaluation, inference and deployment. It is hands-on leadership at the meeting point of research, systems and product, for someone who has shipped ML that real people use and wants to set the standard for how it is done. The client is open on level, from staff through principal.

Role Snapshot

• Location: United States, United Kingdom or Asia (Singapore, South Korea, Indonesia, China)
• Work Model: Hybrid in the US, UK and Seoul for most openings; remote in Singapore, Indonesia and China
• Employment Type: Full-time
• Compensation: Competitive, shared on intro call
• Team: Engineering, machine learning

What You’ll Own

• Take charge of the product’s ML systems across the whole chain: data, training, evaluation, inference and deployment.
• Develop the pipelines that train and fine-tune large models, and keep improving them.
• Build evaluation that measures capability, safety, robustness and how the product actually performs for users.
• Architect fast inference, tuning GPU utilization, memory, latency, cost and reliability.
• Create the data systems that supply high-quality training data, both real-world and synthetic.
• Put in place dependable production infrastructure to deploy, monitor and keep improving models.
• Work with research and application engineering so that model capability turns into product gains.
• Make practical technical trade-offs and iterate quickly on real-world results.

What You Bring

• ML systems you have built and shipped to production for real users, not research prototypes or demos.
• Strong knowledge of how modern large models are trained, fine-tuned, evaluated and served.
• Ease with large models and a clear view of how they fail.
• Strong systems and software engineering fundamentals: you write production-grade code and care that the system is correct.
• Experience running ML workloads at meaningful scale, especially on GPUs.
• Sound technical judgment and the independence to work through ambiguous problems.
• A preference for experimenting, measuring and shipping.
• A high bar for correctness, reliability and production quality.

Why This Role

• Full lifecycle ownership, from training data through to inference in production.
• Serious model work: training and fine-tuning large models on GPU-based systems, in Python with PyTorch and JAX.
• A small, senior team with broad ownership, where engineers use their judgment and execute independently.
• A short hiring process: three to four interviews, assessed by engineers, with a quick answer.

Not the Right Fit If

• Your ML experience is research prototypes or demos. Production systems with real users are the requirement.
• You want to lead from a distance. This lead builds pipelines and inference systems personally.
• You prefer open-ended research to measured, shipped improvements.