• Design, implement, and evaluate RL algorithms for robotic control, motion planning, and adaptive behaviors in dynamic, unstructured environments. • Develop and integrate RL policies with robot control systems, ensuring compatibility with hardware constraints and real-time requirements. • Collaborate with perception teams to fuse RL with vision, depth, and sensor data for robust decision-making. • Build and maintain sim-to-real pipelines, including domain randomization and transfer learning techniques. • Conduct experiments on physical robots, including designing safety protocols and monitoring for unexpected behaviors. • Leverage simulation environments (Isaac Gym, Gazebo, MuJoCo, PyBullet) for large-scale training before real-world validation. • Continuously improve model efficiency to operate within compute and latency constraints on embedded robotic systems.

• Own technical design and delivery of subsystems in a high-throughput, low-latency inference platform capable of handling multi-tenant, enterprise-grade inference workloads. • Develop robust API layers (gRPC, WebSockets, REST, etc.) and developer SDKs that abstract complex distributed inference orchestration into seamless, reliable token streams. • Build and harden a multi-tenant control plane to enable accurate metering, rate limiting, quotas, tenant isolation and noisy-neighbor fairness across the platform. • Optimize inference performance across the entire system stack, including the model engine layer. • Build observability and SLOs to gain insights into system economics, cache-hit rates, GPU utilization and cost accounting per model and per tenant. • Partner with product and infrastructure teams on model onboarding, capacity planning, external API contracts and customer adoption. • Decompose ambiguous work, drive issues to closure, and raise the engineering bar through code quality, reviews, testing, and mentoring.

• Design platform architecture for multi-tenant inference workloads across serving, orchestration, control plane, APIs, SDKs, observability, and model-engine integration. • Develop robust API layers (gRPC, WebSockets, REST, etc.) and developer SDKs that abstract complex distributed inference orchestration into seamless, reliable token streams. • Build and harden a multi-tenant control plane to enable accurate metering, rate limiting, quotas, tenant isolation and noisy-neighbor fairness across the platform. • Optimize inference performance across the entire system stack, including the model engine layer. • Build observability and SLOs to gain insights into system economics, cache-hit rates, GPU utilization and cost accounting per model and per tenant. • Partner with product and infrastructure teams on model onboarding, capacity planning, external API contracts and customer adoption. • Promote Engineering Excellence: Maintain a high bar for engineering excellence in their own work but also set a culture of engineering excellence within the team.

• Design, train, evaluate, and ship ML systems that power governance and security capabilities, starting with problems like prompt injection detection, behavioral anomaly detection, trust scoring, and policy recommendations. • Build the supporting infrastructure: data pipelines, feature stores, model serving, evaluation harnesses, and the feedback loops that make iteration fast. • Make pragmatic build-vs-buy calls. Use frontier models, off-the-shelf tooling, and managed services to move quickly; invest in custom systems where they create durable advantage. • Set technical direction for the team's ML work. Own the architecture, evaluation methodology, model lifecycle, and the bar for shipping. • Help recruit, mentor, and shape the team as it grows. • This role may require participation in a 24/7 on-call rotation for the Agentic Platform; carry genuine pager responsibility for the services you build and operate