[Hiring] Machine Learning Lead Engineer @Next Step Systems

Role Description

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.

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.

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.

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

• Medical insurance
• Dental
• Vision
• Savings Plan Options
• PTO

Compensation

USD 160,000 - USD 190,000 yearly