Applied AI Research Engineer

Netic is the AI revenue engine for essential services who are the backbone of the American economy. With $43M in funding from Founders Fund, Greylock, Hanabi, and Dylan Field who led our Series B, we helped our customers book hundreds of thousands of jobs across services industries in North America. There are now companies operating entirely AI-first on Netic.

As an Applied AI Research Engineer, you’ll dive deep into cutting-edge research, understand the business functions we put on autopilot inside-out, and execute targeted ML projects that deliver pure magic.

What You'll Do:

• Study the frontier: Track frontier work in traditional ML, LLMs, multimodal models, retrieval, and agentic systems—then distill it into ideas we can ship.
• Identify high‑ROI projects: Partner with GTM and ops teams to spot bottlenecks in products; define ML projects that unlock significant leverage for customers.
• Build targeted models: Own the full cycle—data curation, training, evaluation, and deployment—delivering systems that solve real customer pain points.
• Productionize solutions: Integrate models into our real-time platform via robust APIs and streaming pipelines, ensuring model performance and guardrails from day one.
• Self‑direct & ship: Operate like a founder—set technical roadmap, validate quickly, and iterate based on real-world results.

What You'll Bring:

• Deep ML experience: Extensive work with cutting-edge ML techniques; fluent in PyTorch or JAX and modern serving frameworks.
• Research‑to‑revenue record: Proof you’ve taken novel ML concepts from paper → prod with measurable $$ impact or user growth.
• Full‑stack pragmatism: Comfortable with ETL, feature stores, cloud-native infrastructure, and A/B experimentation.
• Data engineering skills: Experience working with complex, real-world data streams and building reliable training pipelines.
• Product intuition: Ability to understand customer workflows and translate business needs into technical solutions.
• Ownership model: You default to action, uphold a high craftsmanship bar, and treat failure modes as learning‑rate multipliers.