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About the Role

We are seeking an Applied ML Engineer to enhance our machine learning systems, focusing on model quality through evaluation datasets, experimentation, and training. You will collaborate with founders and the engineering team to optimize model performance and drive product decisions. This role involves owning significant parts of the model development lifecycle, including dataset management, experiment design, model training and fine-tuning, and result analysis. You will also contribute to research, stay updated on RL techniques, and partner with product and backend teams to integrate models into production.

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 capabilities.
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.

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).
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.
Experience in Golang is a plus, but not required.
Experience with large-scale distributed training is a bonus.
Experience with preference optimization is a bonus.
Experience with synthetic data generation is a bonus.
Experience with evaluation frameworks is a bonus.
Experience with GCP infrastructure is a bonus.
Experience with Temporal is a bonus.
Experience building internal ML tooling is a bonus.

Skills

Applied machine learning
AI
ML model training
ML model evaluation
Reinforcement learning
RLHF
RLAIF
GRPO
PPO
DPO
Dataset creation
Dataset curation
Dataset evaluation
Benchmark design
Labeling strategies
Evaluation methodologies
Experiment design
Data analysis
LLMs
Reasoning models
Software engineering
ML pipelines
ML tooling
Golang
Large-scale distributed training
Preference optimization
Synthetic data generation
Evaluation frameworks
GCP infrastructure
Temporal

Experience Level

3+ years of experience

Salary/Compensations

$170k/year to $280k/year

About the Company

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.