Applied ML Engineer

Macroscope•San Francisco, CA

1d•$170,000 - $280,000

About The Position

We're looking for an Applied ML Engineer to help build and improve the machine learning systems that power Macroscope's core AI capabilities. Your primary focus will be on improving model quality through high-quality evaluation datasets, rigorous experimentation, and model training. You'll work closely with our founders and engineering team to determine which models perform best, why they perform the way they do, and how we can continuously improve them. This is a highly collaborative role where you'll own large parts of the model development lifecycle—from building and maintaining evaluation datasets, to designing experiments, training and fine-tuning models (including reinforcement learning where appropriate), and analyzing results to drive product decisions. You'll also be expected to participate in the research that can push the boundaries of what we are able to do and stay up to date on the latest RL training techniques. You'll also partner closely with our product and backend engineering teams to integrate new models into production and help shape the future of AI-powered software engineering.

Requirements

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 or reinforcement learning for LLMs (RLHF, RLAIF, GRPO, PPO, DPO, or similar techniques). Any practical experience is valuable.
Strong skills in dataset creation, curation, and evaluation, including designing benchmarks, labeling strategies, and evaluation methodologies.
Experience designing and running rigorous experiments, analyzing results, and using data to drive model improvements.
Familiarity with LLMs, reasoning models, and the rapidly evolving open-source model ecosystem.
Strong software engineering skills and experience building reliable ML pipelines and tooling.
Comfortable working in a fast-paced, high-agency startup environment where priorities evolve quickly and everyone helps define what to build next.

Nice To Haves

Experience in Golang (the primary language of our backend systems) is a plus, but not required.
Experience with large-scale distributed training.
Experience with preference optimization.
Experience with synthetic data generation.
Experience with evaluation frameworks.
Experience with GCP infrastructure.
Experience with Temporal.
Experience building internal ML tooling.

Responsibilities

Improving model quality through high-quality evaluation datasets, rigorous experimentation, and model training.
Determining which models perform best, why they perform the way they do, and how to continuously improve them.
Building and maintaining evaluation datasets.
Designing experiments.
Training and fine-tuning models (including reinforcement learning where appropriate).
Analyzing results to drive product decisions.
Participating in research to push the boundaries of what is possible.
Staying up to date on the latest RL training techniques.
Partnering with product and backend engineering teams to integrate new models into production.
Helping shape the future of AI-powered software engineering.