Member of Technical Staff - Machine Learning Infrastructure Engineer

United States Digital Space LLC; Toronto; On-site; CAD 253,200 - 422,001; Full time

Benefits offered by this job

Health, vision, dental, benefits

401K match

Lunch onsite

Weekly snack orders

Visa sponsorship & relocation support

Equity

Job summary

United States Digital Space LLC is seeking ML Infrastructure Engineers to design, build, and scale the compute, scheduling, and data infrastructure powering post‑training research on large language models. You will shape how our research runs and help enable high‑throughput, low‑latency workflows.

Candidates should have strong software engineering fundamentals, experience building production‑grade ML infrastructure, and proficiency with PyTorch or JAX, cloud platforms (AWS/GCP), and Kubernetes.

Qualifications

• Strong software engineering fundamentals.

Responsibilities

• Design, build, and scale 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 to accelerate reproducible experimentation.
• Build evaluation, benchmarking, monitoring, logging, and deployment tooling to ensure reliability at scale.
• Partner with Research Engineers to translate research needs into infrastructure requirements and ship fast in response to feedback.

Skills

ML infra design

Production-grade infra

PyTorch/JAX

Distributed systems

Cloud platforms (AWS/GCP)

Kubernetes

Data pipelines

LLM training/inference familiarity

Communication with researchers

Tools

PyTorch

JAX

Kubernetes

vLLM

SGLang

Job description

About Us

the company 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.

About the Role

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.

What You Will Do

• 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

What We are Looking For

• 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
• Understanding of distributed systems principles, and 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
• Some familiarity with LLM training/inference internals (transformers, distributed training, inference libraries like vLLM or SGLang) – expertise is a plus, not mandatory
• Ability to balance production rigor with the pace of fast‑moving research, and communicate infrastructure trade‑offs clearly to researchers who aren't infra specialists

What We Offer

• 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.

Compensation Range: $180K - $300K