Forward Deployed Engineer

• Convert 2-3 active customer evaluations into committed design wins by getting customer workloads running at target latency and accuracy on Chimera.
• Port customer workloads onto Chimera - architect, adapt, profile, and write custom kernels when required.
• Work on-site and remotely with customer engineering teams: debug failures, hit benchmark targets, and guide model-architecture tradeoffs.
• Drive evaluations forward end-to-end, from first technical conversation through design win.
• Codify what you learn into reusable assets - SDK skills, porting playbooks, and agentic content - so each subsequent engagement moves faster than the last.
• Build applications and benchmark collateral that support future customer engagements.
• Partner with engineering and product teams to relay customer learnings and shape the roadmap.
• Contribute skills and playbooks to Quadric's growing agentic FDE toolset.

• Strong C++ and Python; has written performance-critical code for a constrained target (DSP, NPU, GPU, or embedded SoC).
• Systems-level performance intuition - memory bandwidth, data movement, cycle-level reasoning - and can read a profile and know where the time went.
• High agency and independent problem-solving; methodical and empirical rather than theoretical.
• Comfortable operating without a playbook and in ambiguity - this role is still being defined.
• Prior FAE, Sales Engineering, or FDE title - or startup experience as the de facto owner of an end-to-end implementation.
• Familiarity with agentic dev tooling (Claude Code, MCP) - or strong enthusiasm to adopt it.
• Background at NPU/edge-AI IP and silicon companies (e.g., Qualcomm SNPE/QNN, NVIDIA TensorRT/Jetson field engineering, Ceva, Cadence Tensilica, Synopsys ARC, Arm, Hailo, Ambarella, SiMa.ai, Axelera, Edge Cortix).
• Background at inference-focused startups (e.g., Groq, Untether, Tenstorrent) in field/customer engineering or kernel teams.
• Palantir-style FDE experience - candidates with embedded or ML-systems backgrounds only.
• Experience at technical trading arms optimizing kernels or hardware for speed (e.g., DRW, Hudson River, D.E. Shaw, Citadel).
• Big-firm consulting background, provided the candidate personally did the hands-on technical work rather than managing it.

Demonstrates strong expertise in C++ and Python for performance-critical code development, with a focus on systems-level performance and independent problem-solving in ambiguous environments. Experience in customer engineering and the ability to drive evaluations from technical conversations to design wins are essential.

• C++ Programming
• Python Programming
• Systems-Level Performance Intuition
• Customer Engineering Experience
• Agentic Dev Tooling Familiarity

• Performance-Critical Code Development
• Kernel Optimization
• Memory Bandwidth Management
• Data Movement Analysis
• Cycle-Level Reasoning

• Independent Problem-Solving
• High Agency
• Methodical Approach
• Empirical Analysis

• Field Application Engineering
• Sales Engineering
• Inference-Focused Startups
• Edge-AI IP
• Silicon Companies

• Chimera
• Claude Code
• MCP
• DSP
• NPU
• GPU
• Embedded SoC