Machine Learning Engineer

Axe Recruiting; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Axe Recruiting represents a well-funded AI applications company building production ML systems for a global market. Three profiles — core ML, senior ML, and ML platform — work on live systems from day one, balancing accuracy, latency and reliability. Remote-friendly, with hybrid options in select locations.

You’ll own, deploy and improve ML components across data, training, evaluation and inference. Expect hands-on model development, production-quality code and mentoring responsibilities as you

Qualifications

• Strong fundamentals in machine learning and modern neural architectures, and production-quality code.
• Comfort with ambiguity in a fast-moving setting, and a bias toward shipping and iterating.
• Core ML track: hands-on experience with training, fine-tuning or deploying models.
• Core ML track: curiosity, coachability and ownership over time.
• Senior track: ownership of critical ML subsystems and mentorship.
• Platform track: experience building ML infrastructure or production ML systems.
• Platform track: experience with deployment, inference, data pipelines and distributed systems.

Responsibilities

• Develop and improve ML components spanning data, training, evaluation and inference.
• Adapt and fine-tune models as parts of larger production systems.
• Write tests, evaluations and benchmarks to reveal model behavior.
• Build and maintain pipelines for real-world and synthetic data.
• Track down model failures and production incidents from live signals.
• Platform track: run infrastructure for training, deployment and model serving.
• Senior track: own critical ML subsystems and mentor engineers.
• Release improvements in small steps and learn from users.

Skills

ML systems

Production code

Distributed systems

Model deployment

Data pipelines

Python

PyTorch

JAX

Tools

vLLM

SGLang

TensorRT-LLM

Python

PyTorch

Job description

Three ML engineering profiles on one team (core ML, senior ML and ML platform), all working on real production systems from day one.

The Opportunity

Our client puts ML engineers on live production systems immediately, not on research prototypes. 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 search covers three profiles. The core ML engineer builds components across data, training, evaluation and inference. The senior ML engineer independently owns critical ML subsystems. The ML platform engineer builds the infrastructure behind training, serving and observability. All three work inside the limits production imposes: cost, latency, safety and reliability.

Role Snapshot

• Location: United States, United Kingdom, Europe (Ireland, Germany, Poland, Portugal, Spain, Sweden, Switzerland) or Asia (Singapore, South Korea, China)
• Work Model: Remote in most locations; hybrid in the UK, Zurich and Seoul
• Employment Type: Full-time
• Compensation: Competitive, shared on intro call
• Team: Engineering, machine learning

What You’ll Own

• Develop and improve ML components spanning data, training, evaluation and inference.
• Adapt and fine-tune models as parts of larger production systems, and turn research ideas into systems that stay up.
• Write the tests, evaluations and benchmarks that reveal how models behave and flag regressions.
• Build and look after pipelines for real-world and synthetic data.
• Track down model failures, performance issues and production incidents from live signals.
• Platform track: run the infrastructure for training, deployment and model serving, delivering high-throughput, low-latency inference with monitoring, tracing and alerting.
• Senior track: take full ownership of critical ML subsystems, and mentor and review other ML engineers.
• Release improvements in small steps and learn from what real users do.

What You Bring

• All tracks: strong fundamentals in machine learning and modern neural architectures, and clean, production-quality code.
• All tracks: ease with ambiguity in a fast-moving setting, and a preference for shipping, iterating and improving.
• Core ML track: some hands‑on experience training, fine‑tuning or deploying models.
• Core ML track: curiosity, coachability and speed with new tools; you can work through ambiguity with guidance and take on more ownership over time.
• Senior track: ML systems you built and shipped for real users, and a clear understanding of how modern models behave, and misbehave, in production.
• Senior track: systems thinking over scripts, with ownership, independence, fast learning and clear communication.
• Platform track: strong software engineering fundamentals and experience building ML infrastructure, platforms or production ML systems.
• Platform track: experience with model deployment, inference, evaluation or data pipelines, and a strong grasp of distributed systems and reliability.

Why This Role

• Production from the first day, with work measured against accuracy, latency and reliability targets.
• A current stack: Python, PyTorch and JAX on GPUs, with serving infrastructure such as vLLM, SGLang or TensorRT-LLM on the platform side.
• A real growth path, with an entry point for strong earlier‑career engineers and a senior seat that carries mentorship.
• A small, hands‑on team and a short hiring process of three to four interviews.

Not the Right Fit If

• You want a research role. This team ships, debugs & iterates on live systems.
• You are not prepared to write production-quality code.
• You need a settled environment with fixed requirements.