Applied Researcher

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.