Netic provides an autonomous AI revenue engine that automates client outreach, bookings, marketing and analytics for home-service businesses such as plumbing, roofing and electrical contractors.
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. You’ll join our team with relentless builders from Scale, Databricks, HRT, Meta, MIT, Stanford, and Harvard in bringing frontier AI to the physical economy, where the problems are hard, the data is complex, and the impact is immediate and tangible. 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: 4+ years 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. What brings us together is our commitment to: - Live to build - Run through walls and win - Obsess over customers in each line of code - Lose sleep over the "almost perfect" - Show internal locus of control - Prioritize finesse: refinement of first principles thinking, execution, and craftsmanship We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.