Staff Machine Learning Engineer

About Scowtt

Scowtt is an early-stage startup transforming the way businesses convert leads into customers through AI/ML marketing optimization and fully autonomous sales experiences. By integrating CRM, web signals, and product interaction data into a real-time conversion model, we help businesses turn interest into action—immediately. We’re growing fast, and onboarding new customers quickly and cleanly is core to our success.

About the Role

We are looking for a Staff+ Machine Learning Engineer to lead and evolve the ML systems that power our Marketing AI and AI Sales Agents.

This is a hands-on technical leadership role owning ML end-to-end - from model architecture and experimentation to production, scale, and long-term system health. You will build core IP: models that directly influence conversion rates, ROAS, and customer value across major ad networks.

What You’ll Do

• End-to-end ownership of ML systems, from research and modeling to deployment and scale
• Design and evolve next-generation conversion and value prediction models using behavioral, CRM, and engagement data
• Fine-tune and specialize LLMs (e.g., SFT, LoRA, PEFT) to power AI Sales Agents focused on reasoning, persona alignment, and long-term conversion outcomes
• Drive high-speed feedback loops between ML systems and ad platforms
• Design systems for continuous training, monitoring, and rapid experimentation
• Solve systemic ML problems around data quality, drift, reliability, and velocity
• Mentor senior ML engineers and raise the organization’s ML bar

Qualifications:

Must Have

• Bachelor’s degree in Computer Science, ML, Data Science, or equivalent
• 6-10 years of experience in applied machine learning and production ML systems
• 6-10 years of experience in deploying and operating ML models at scale
• 6-10 years of experience and strong hands-on skills with Python and modern ML frameworks (PyTorch, TensorFlow)

Should Have

• Experience designing models for prediction, ranking, recommendation, or personalization
• Strong understanding of LLM fine-tuning techniques and production usage
• Ability to operate across research, experimentation, and high-scale production systems
• Proven ability to influence technical direction through architecture and execution

Nice to Have

• Experience with transformer-based sequence models or multimodal architectures
• Familiarity with ad-tech, mar-tech, sales-tech, or CRM ecosystems
• Experience building ML platforms, experimentation systems, or MLOps tooling
• Experience leading multi-quarter ML initiatives

What Success Looks Like

• ML systems materially improve customer ROI and conversion outcomes
• Teams move faster because of your systems and decisions
• Technical architecture scales cleanly with product ambition
• You create durable ML leverage across the company