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
