Merge PRs faster and fix bugs before they reach production. Macroscope provides AI-powered code review, automated PR descriptions, and real-time status updates for modern development teams. Backed by Lightspeed.
About the role
We're looking for an Applied ML Engineer to improve the models and ML systems behind Macroscope's core AI capabilities. You'll work closely with our founders and engineering team to build evaluation datasets, run experiments, train and fine-tune models, and determine what meaningfully improves model performance.
What they're looking for
- 3+ years of experience in applied machine learning, AI, or related engineering roles
- Experience building, training, fine-tuning, or evaluating modern ML models in production or research environments
- Experience with reinforcement learning for LLMs, including RLHF, RLAIF, GRPO, PPO, DPO, or similar techniques
- Strong experience with dataset creation, curation, benchmarks, labeling strategies, and evaluation methodologies
- Experience designing rigorous experiments and using results to improve model performance
- Familiarity with LLMs, reasoning models, and the evolving open-source model ecosystem
More about this role
Macroscope is building the infrastructure for the next generation of software development, where engineers direct fleets of agents, every change is automatically reviewed and verified, and organizations have a clear understanding of how their products and codebases are evolving.
Our mission is to give engineers more leverage and leaders more clarity, so teams can build better software, faster.
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.
We're looking for an Applied ML Engineer to improve the models and ML systems behind Macroscope's core AI capabilities. You'll work closely with our founders and engineering team to build evaluation datasets, run experiments, train and fine-tune models, and determine what meaningfully improves model performance.
You'll have ownership across the ML lifecycle, including model evaluation, reinforcement learning, and bringing new approaches into production. You'll also...
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