As an Applied ML Engineer at Macroscope, you will own the machine learning lifecycle for our agentic software development infrastructure. You will focus on training and fine-tuning LLMs using advanced reinforcement learning techniques like GRPO and DPO. By building high-quality evaluation datasets and rigorous benchmarks, you’ll drive model improvements that empower engineers with unprecedented leverage.
Applied ML Engineer at Macroscope
Macroscope is building the next generation of software development infrastructure, enabling engineers to direct fleets of agents with automated verification. Founded by repeat exited founders from Periscope and Magic Pony and backed by Lightspeed and Thrive, we are looking for an Applied ML Engineer in San Francisco to lead RL training and fine-tuning. You'll own the model development lifecycle, from designing reward functions to scaling synthetic data, all while earning significant early equity. Join a high-agency team where you'll do technically deep work to fundamentally change how code is written, reviewed, and understood.
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Location
San Francisco, United States
Compensation
$170k-$280k + Equity
Company
Macroscope
Role overview
Prasso.ai (also branded as Macroscope) is an AI-powered engineering intelligence platform for software development teams. It provides an AI code review and understanding engine that connects to a team’s codebase and tools like issue trackers to synthesize, summarize, and answer what is happening in the code. The platform automatically tracks changes across an engineering organization, offers high-signal AI-based code reviews, and includes agents that can answer questions, provide on-demand insights in place of status meetings, and perform actions to help leaders merge code faster with fewer bugs while saving engineers time. Its flagship product, Macroscope, orchestrates cloud agents that write code, verify their work in isolated sandboxes, and leverage accumulated engineering knowledge to improve developer productivity and give leaders clarity on their software projects.
What you will do
- Train and fine-tune LLMs using reinforcement learning techniques such as GRPO, DPO, and PPO to improve reasoning and code generation capabilities.
- Design and curate high-quality evaluation datasets and benchmarks to rigorously measure model performance and guide product-driven experimentation.
- Collaborate with backend engineers to integrate custom models into production using a stack featuring Golang, Temporal, and custom-built AST code walkers.
Who this is a fit for
- Has 3+ years of experience in applied ML, specifically building, fine-tuning, or evaluating modern models in production or research environments.
- Possesses hands-on experience with reinforcement learning for LLMs (RLHF, RLAIF, or similar) and a deep understanding of training dynamics and reward shaping.
- Demonstrates strong software engineering skills and a high-agency mindset, comfortable building reliable ML pipelines in a fast-paced, in-office San Francisco startup.
Why this role is remarkable
- You will perform deep technical work in reinforcement learning (RLHF, DPO, GRPO) and model training, moving beyond simple API wrappers to build core agentic infrastructure.
- Macroscope is led by veteran founders who previously exited Periscope and Magic Pony, and is supported by top-tier investors including Lightspeed, Thrive Capital, and Google Ventures.
- You’ll receive significant early-stage equity (0.25%-0.5%) at a valuation that offers massive upside compared to later-stage competitors, working in a high-agency, flat-structured environment.
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