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Salary: USD 170,000 - 280,000 / annual
Macroscope is building the source of truth for software companies—helping leaders understand how their products and codebases are evolving through AI-powered insights grounded in code analysis. Founded by serial entrepreneurs and backed by Lightspeed, Thrive Capital, Google Ventures, and Adverb, the company is assembling a small, high-agency team to build something transformative.
As an Applied ML Engineer, you'll own large parts of the model development lifecycle for Macroscope's core AI capabilities. Your primary focus will be improving model quality through high-quality evaluation datasets, rigorous experimentation, and model training. You'll work directly with founders and the engineering team to determine which models perform best, understand why, and drive continuous improvement.
Key responsibilities include building and maintaining evaluation datasets, designing experiments, training and fine-tuning models (including reinforcement learning techniques like RLHF, RLAIF, GRPO, PPO, DPO), analyzing results to drive product decisions, and participating in research to push the boundaries of AI-powered software engineering. You'll also partner closely with product and backend engineering teams to integrate new models into production.
You bring 3+ years of applied ML, AI, or related engineering experience. You have hands-on experience building, training, fine-tuning, or evaluating modern ML models in production or research environments, with practical experience in reinforcement learning or RLHM techniques. You're skilled in dataset creation, curation, and evaluation—designing benchmarks, labeling strategies, and evaluation methodologies. You can design and run rigorous experiments, analyze results, and use data to drive improvements. You're familiar with LLMs, reasoning models, and the open-source model ecosystem, with strong software engineering skills for building reliable ML pipelines and tooling.
The role demands high agency, ownership mentality, and comfort in a fast-paced startup environment where priorities evolve quickly. You're self-motivated, willing to work extremely hard on high-risk ventures, and passionate about the mission. Experience with Golang (the backend language), large-scale distributed training, preference optimization, synthetic data generation, GCP infrastructure, Temporal, or internal ML tooling is a plus.
