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Develop and optimize lightweight machine learning models for edge AI applications, integrating into production systems and mentoring junior engineers.

The role involves leading RTL design and verification for ASIC/SoC products, mentoring junior engineers, and performing PPA analysis to optimize designs.

The Business Development Lead will drive revenue growth by aligning TetraMem's technology with customer solutions, manage long sales cycles, and develop key industry relationships.

Support deployment and integration of low-power edge inference systems, troubleshoot hardware/software issues, and assist clients in optimization of ML models.

Lead full-cycle recruiting and onboarding, partner with managers on hiring and job strategies, manage employee relations and performance support, maintain HRIS and compliance (I-9/E-Verify), administer compensation, benefits and immigration processes (H-1B, TN, CPT, OPT, green card), and support HR programs, analytics, and system implementations.

The role involves developing mixed-signal verification plans, creating testbenches, writing test cases, collaborating with design teams, and optimizing verification methodologies for IC designs.

Support the design, verification, and integration of digital systems in AI accelerators through RTL design and simulations.

Assist with design and validation of analog circuits, perform simulations, and support silicon validation and performance testing.

Develop and optimize compiler, runtime, and SDK components for TetraMem's analog compute-in-memory AI hardware. Translate ML models into efficient executables, implement low-level optimizations and debugging tools, integrate ML frameworks and deployment pipelines, benchmark and profile workloads, support hardware bring-up and validation, and collaborate across teams to improve system performance.

Develop and optimize Python and C++ software to compress, convert, deploy, and run neural networks on analog compute-in-memory hardware. Implement quantization, pruning, graph optimizations, runtime APIs, and profiling tools. Collaborate with researchers and hardware engineers, perform testing, debugging, and CI, and support research, documentation, and validation of hardware-software co-design for edge and embedded AI systems.