Principal Machine Learning Engineer

Sarsfield Group; United States; Vor Ort; USD 230.000 - 355.000; Vollzeit

Benefits dieser Stelle

Health and Dental

Life Insurance

401(k) Matching

Equity

Zusammenfassung

Sarsfield Group is seeking a Principal Machine Learning Engineer to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will work across the model lifecycle—from data and training to inference and deployment—leading hands-on efforts at the intersection of research, systems, and product.

Ideal candidates ship real ML systems at scale, with GPU-based training and inference experience, strong production-grade code, and

Qualifikationen

• Experience building production ML systems.
• Strong knowledge of large-model training, fine-tuning, evaluation and inference.
• Excellent software engineering and systems fundamentals.
• Experience with GPU-based ML workloads.
• Ability to make pragmatic trade-offs and iterate quickly.

Aufgaben

• Own end-to-end ML systems from data to deployment.
• Build and maintain training and fine-tuning pipelines.
• Design evaluation systems for capability, safety and performance.
• Architect high-performance inference systems with optimized latency and cost.
• Collaborate with research and application engineering to ship product improvements.
• Ensure reliability and monitor production models.

Kenntnisse

Python

PyTorch

JAX

GPU-based training

Jobbeschreibung

Founded in 2019, we’re the leading insurance platform in Southeast Asia, helping millions of people find the best-value coverage online. Now we’re expanding into saving, investing, spending and more, all with one goal: helping people get more from their money every day. Join our diverse, global team and help make financial services simpler for everyone.

About the Role

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they’re not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations

Our objective is to organise anyone’s life, allowing us all to spend time on valuable and meaningful things

As Principal Machine Learning 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.

What You’ll Own

Own the end-to-end 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.

What We’re Looking For

Experience building and shipping 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 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.

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.

Python

PyTorch / JAX

GPU‑based training and inference system

Ideal Experience

You have built or shipped real 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.

How We Work

We are a small, high‑talent‑density, hands‑on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently.

We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.

Compensation:

US: $230,000 – $355,000 USD

CAD: $200,000 – $310,000 CAD

US: Health and Dental, Life insurance, 401(k) matching, and equity

Canada: Health and Dental, Life Insurance, equity

We thank all applicants for their interest; however, only those selected for an interview will be contacted.