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 Senior 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.
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
Have strong software engineering fundamentals, experience building production‑grade infrastructure (ideally for ML or data‑intensive systems), and proficiency in core ML frameworks such as PyTorch or JAX
Significant experience and understanding of distributed systems principles, and have hands‑on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high‑throughput, low‑latency workloads
Have experience with data engineering tools and building robust, scalable data pipelines
Have some familiarity with LLM training/inference internals (transformers, distributed training, inference libraries like vLLM or SGLang) — deep expertise is a plus, not a requirement
Can balance production rigor with the pace of fast‑moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists
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
We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.
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Member of Technical Staff - Machine Learning Infrastructure Engineer, Post-training • Toronto, ON, Canada
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