
Visionary Machine Learning Architect
13 hours ago
Embark on a transformative journey as a pioneering Senior Machine Learning Engineer at Truxt.ai.
We are disrupting the status quo by developing innovative AI-driven autonomous systems that revolutionize software operations.
You will be instrumental in designing and implementing cutting-edge ML solutions, directly influencing our core infrastructure, model deployment strategies, and data engineering decisions.
You'll have the autonomy to research, experiment, and deploy novel ML solutions while building substantial ownership in our company's future.
What Sets This Opportunity Apart:
- Unfettered Freedom: Set your own goals, timelines, and approaches without bureaucratic hurdles.
- Meaningful Ownership: Founding engineers receive significant equity packages, ensuring you directly benefit from the value you create.
- Tangible Impact: Solve complex real-world problems with far-reaching consequences.
- Culture of Collaboration: Directly influence product direction, company culture, and growth strategy.
- Total Transparency: 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.
Your Qualifications:
Machines Learning Expertise
- Passionate Problem-Solver: Proactive expert who identifies, iterates, and ships innovative solutions.
- ML Veteran: 5+ years building and deploying production ML systems at scale.
- Deep Learning Specialist: Hands-on experience with transformer architectures, LLMs, and generative AI.
- MLOps Mastery: Expert in ML pipeline orchestration, model monitoring, and A/B testing frameworks.
- Programming Excellence: Proficiency in Python, with experience in PyTorch/TensorFlow, and familiarity with systems languages (Go, Rust, C++).
Technical Requirements:
- 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.
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