Member of Technical Staff - Machine Learning Infrastructure Engineer

Preference Model • Toronto, ON

26d • $180,000 - $300,000 • Onsite

About The Position

Preference Model is automating ML engineering, and a critical component is models' abilities to develop software. The way we build software is changing fast. Five years ago we wrote every line of code by hand. Today, we don't. What does our work look like five years from now? We are shaping this future. Recent models work well on narrow tasks but are still brittle on real software work: large codebases with real conventions and technical debt, judgment-heavy design decisions, and multi-step problems. The bottleneck on fixing that is the supply of hard, high-fidelity scenarios that find where the best models still break. That is what we build. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

Frontier research moves only as fast as its infrastructure permits. Building solid infrastructure is foundational to our mission of pushing self-directed learning as far as it can go. We are looking for ML Infrastructure Engineers to build the systems that power the frontier of post-training on large language models. This role involves building scalable infrastructure to enable high-throughput systems and shape how our research is run, bringing us closer to models that can train themselves on what they aren't yet good at.

Requirements

• Strong software engineering fundamentals
• Experience building production-grade infrastructure (ideally for ML or data-intensive systems)
• Proficiency in core ML frameworks such as PyTorch or JAX
• Understand distributed systems principles
• Hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high-throughput, low-latency workloads
• Experience with data engineering tools and building robust, scalable data pipelines
• Can balance production rigor with the pace of fast-moving research
• Communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists

Nice To Haves

• Some familiarity with LLM training/inference internals (transformers, distributed training, inference libraries like vLLM or SGLang) — deep expertise is a plus, not a requirement

Responsibilities

• Design, build, and scale the compute, scheduling, and data infrastructure that powers post-training research on our in-house RL environments
• Develop and maintain core ML framework primitives and internal tooling that researchers rely on daily, accelerating reproducible experimentation and reducing time from idea to result
• Build evaluation and benchmarking infrastructure, monitoring, logging, and debugging tooling, and automated testing and deployment systems, so failures are caught early and infrastructure stays reliable as it scales
• Partner directly with Research Engineers to translate research needs into infrastructure requirements, and ship fast in response to their feedback

Benefits

• Competitive cash and equity compensation (>90th percentile)
• Ownership and autonomy in a fast moving startup environment
• Opportunity to work alongside senior and staff engineers from frontier labs and infrastructure companies, plus top ML engineers
• Health, vision, dental, benefits
• 401K match
• Lunch provided everyday onsite
• Weekly snack orders
• Visa sponsorship & relocation support available

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed