About the role
Responsibilities:
- Ship production code used by real customers.
- Own well-scoped engineering problems end-to-end.
- Work with senior engineers on architecture and system design.
- Debug and improve production systems.
- Build reliable AI infrastructure and applications.
- Learn quickly and expand your technical scope.
- Work across engineering, infrastructure, AI, and customer-facing problems.
Requirements:
- You have 2 - 5 years of professional engineering experience and have shipped production software.
- You should be able to take ownership of a well-defined component from design, implementation, deployment, debugging, and improvement.
- We are looking for engineers with strong fundamentals rather than candidates who simply have a long list of AI tools on their resume.
- AI evaluation, post-training, simulations, and data curation.
- Best suited for ML engineers who combine strong engineering fundamentals with hands-on experimentation.
- Relevant experience includes Python, PyTorch or JAX, LLM evaluation and benchmarking, fine-tuning/post-training, agent evaluation, RL/RLHF, embedding and reranker experiments, simulation environments, synthetic data generation, large-scale data curation, model training or serving infrastructure, statistical experimentation and evaluation
- We value research that ships into production, rather than research or prototypes that remain isolated from real-world systems.
The strongest candidates will demonstrate:
- Real production ownership, not just project experience.
- Strong coding and systems fundamentals.
- Hands-on Docker/Kubernetes/cloud experience.
- Genuine AI/ML exposure relevant to one of the three tracks.
- Ability to explain technical decisions and trade-offs.
- Strong debugging and problem-solving ability.
- Curiosity and willingness to work across the stack.
- Ability to operate effectively in a fast-moving startup environment.