About us
We're solving large enterprises' biggest data paradoxes and pioneering fully autonomous software operations.
As an early-stage company driven by a meaningful mission, we seek exceptional engineering talent who values purpose, long-term impact, and significant equity ownership over traditional salary compensation.
The Role
You'll architect our AI-driven autonomous systems, influencing core ML infrastructure, model deployment strategies, and data engineering decisions.
Freedom to Execute :
- Set your own goals, timelines, and approaches without bureaucratic hurdles.
- True Ownership : Founding engineers receive substantial equity packages (competitive with senior engineer salaries at scale), ensuring you directly benefit from the value you create.
- Technical Challenge : Solve complex real-world problems with meaningful impact.
- Impact-Driven Culture : Directly influence product direction, company culture, and growth strategy.
- Transparent Leadership : Collaborative environment, weekly strategy sessions, and full visibility into company health and roadmap.
- Exponential Upside Potential : Your equity stake grows with company success—potential for life-changing returns as we scale.
Requirements
Passionate about building something extraordinary and willing to accept equity-only compensation during our early growth phase. You must possess the following skills :
Key Skills
ML Expertise :Proactive problem-solver who identifies, iterates, and ships solutions.5+ years building and deploying production ML systems at scale.Hands-on experience with transformer architectures, LLMs, and generative AI.Expert in ML pipeline orchestration, model monitoring, and A / B testing frameworks.Proficiency in Python, with experience in PyTorch / TensorFlow, and familiarity with systems languages (Go, Rust, C++).Enterprise AI : Experience deploying ML models in enterprise environments with strict SLAs.Autonomous Systems : Background in reinforcement learning, multi-agent systems, or autonomous decision-making.Data Engineering : Comfortable with large-scale data processing, feature engineering, and real-time inference.Cloud ML Platforms : Experience with AWS SageMaker, Google AI Platform, or Azure ML.Model Optimization : Knowledge of model compression, quantization, and edge deployment.