Machine Learning Lead Engineer, Machine Learning (ML) Required, Work From Home - G

Next Step Systems; Deutschland; Remote; EUR 90.000 - 150.000; Vollzeit

Benefits dieser Stelle

Medical insurance

Dental

Vision

Savings plan

Paid time off

Zusammenfassung

Next Step Systems is seeking a Machine Learning Lead Engineer to own the end-to-end ML systems powering our intelligence, from data and training to deployment. This is a hands-on leadership role operating at the intersection of research, systems, and product.

The role emphasizes production-grade code, scalable pipelines, and the ability to ship real product improvements through data-driven decisions. This position is 100% remote and includes a coding assessment requirement.

Qualifikationen

• Experience building and shipping ML systems in production.
• Strong software engineering and systems fundamentals.
• Experience with GPU-based training/inference.

Aufgaben

• Own end-to-end ML pipelines from data to deployment.
• Design scalable training and fine-tuning workflows.
• Develop robust evaluation and monitoring for models.
• Collaborate with research and product teams to ship features.

Kenntnisse

ML systems

Python

GPU-based training

Production deployment

Research-to-prod

Tools

PyTorch

JAX

Jobbeschreibung

Machine Learning Lead Engineer, Machine Learning (ML) Required, Work From Home

As Machine Learning Lead Engineer, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands‑on leadership role for someone who wants to operate at the intersection of research, systems, and product. ++Machine Learning (ML) experience is required. This position is 100% Remote.++

++MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT.++

Machine Learning Lead Engineer Responsibilities:

• Own the end-to-end Machine Learning (ML) systems powering our company, from data and training to evaluation, inference, and deployment.
• Build and evolve training and fine‑tuning pipelines for large models.
• Design evaluation systems that measure capability, robustness, safety, and real-world product performance.
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.
• Build data pipelines and systems for high-quality real-world and synthetic training data.
• Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.
• Partner closely with research and application engineering to turn model capabilities into product improvements.
• Make pragmatic technical trade‑offs and rapidly iterate based on real-world performance.

Outcomes

• Research and models reliably translate into production-ready solutions with clear performance and quality targets.
• ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Production issues are detected, debugged, and resolved quickly, minimizing user impact.
• Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
• Iterations on models and systems are measurable, safe, and improve user experience over time

Tech Stack: Python, PyTorch/JAX, and GPU-based training and inference system.

Machine Learning Lead Engineer Qualifications:

• Machine Learning (ML) experience is required.
• Experience building and shipping Machine Learning (ML) systems used in production, not just research prototypes.
• Strong understanding of modern large-model training, fine‑tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals.
• Experience operating Machine Learning (ML) workloads at meaningful scale, particularly GPU-based systems.
• Strong technical judgment and the ability to navigate ambiguous problems independently.
• A bias toward experimentation, measurement, and shipping.
• High standards for correctness, reliability, and production quality.

Machine Learning Lead Engineer Ideal Experience:

• You have built or shipped real Machine Learning (ML) systems used by people, not just demos.
• You are comfortable working with large models and understanding their failure modes.
• You write strong, production‑grade code and care about system correctness.

Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.

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