Senior Applied ML Engineer

About Macroscope

Macroscope aims to be the source of truth of what's happening for any company that builds software. Our mission is to give leaders clarity and engineers time.

We help leaders understand how their products and codebases are evolving-what's changing, who's working on what, and where progress is happening-grounded in the ultimate source of truth : the code.

Macroscope is founded by former entrepreneurs who have started and sold multiple companies, and operated as product / engineering executives at public tech companies. We're fortunate to be supported by the best VC firms and angels in the business, including Lightspeed Venture Partners, Thrive Capital, Google Ventures, and Adverb.

About the role

We're looking for a Senior Applied ML Engineer to design, build, and optimize the machine learning and AI systems that power Macroscope's core features. You will have ownership and agency of our systems from end to end - from data collection and evaluation, to model experimentation, to productionizing our systems at scale.

This is a deeply cross-functional role where you'll own the ML / AI lifecycle of one of the most critical surface areas of our product : AI Code Review . Working alongside our co-founders, you will be responsible for making the decisions that will determine how we build and improve the product, everything from building high-quality datasets, running experiments, interpreting results, and making architectural decisions to improve model performance. You will also play a lead role in designing and building the software that integrates the models with our backend application and product experience. This is a unique opportunity to have an immense amount of impact on how we evolve our product.

Our technology stack : Typescript / React (front-end), Golang (backend), Temporal, Google Cloud (GCP), Postgres, Terraform, custom-built AST "code walkers" in various languages (Golang, Typescript, Swift, Python, Rust)

Qualifications

• 8+ years of experience in applied ML, data science, or ML infrastructure roles.
• Proven track record of designing, training, and deploying ML models in production at scale.
• Strong skills in data curation and evaluation - understanding what data to collect, how to clean and label it, and how to measure success.
• Experience writing production-grade code and building robust pipelines connecting data → models → backend systems .
• Deep intuition for model behavior and data representation - you know how to experiment, interpret results, and iterate effectively.
• Experience architecting complex distributed systems and optimizing them for performance and reliability.
• Hands-on experience with common ML / AI frameworks (e.g. PyTorch, TensorFlow) and working knowledge of LLMs and RAG systems.
• Comfortable in a fast-paced, high-agency startup environment , where priorities shift and you help define what to do next.
• Experience in Golang (the primary language of our backend systems) is a strong plus, but is not required.
• Bonus : experience with GCP infrastructure, working with Temporal for workflow orchestration, or building internal ML platforms.

About you