Top Tech Jobs & Startup Jobs in Boston, MA

Develop and evaluate multimodal deep learning and reasoning models for Edge AI, covering data collection, annotation, preprocessing, training, post-training, reinforcement learning, knowledge distillation, and rigorous evaluation. Build agentic and multi-agent reasoning systems, modify transformer architectures, and prototype in Python using PyTorch or TensorFlow. Document findings through reports, publications, and conference submissions while exploring emerging approaches such as world models, JEPA, diffusion, and deep research automation.

The Staff Engineer will architect and develop mixed-signal ICs and SDR chips, managing requirements and collaborating with cross-functional teams to ensure successful project execution.

Lead demand forecasting, capacity planning, and supply alignment for semiconductor foundry operations. Build models, analyze trends, track performance metrics, support cross-functional planning cycles, prepare executive reports, and advise on wafer allocation, supplier negotiations, and process improvements to optimize capacity utilization and supply continuity.

Develop embedded AI deployment infrastructure and model optimization tools for heterogeneous SoCs. Responsibilities include building end-to-end model deployment workflows, hardware-aware neural architecture design, quantization, pruning, compilation, code generation, and embedded platform integration. The role also explores agentic AI for model-hardware co-optimization and requires collaboration across AI, embedded systems, compiler, and hardware teams.

Design and develop analog and mixed-signal integrated circuits, including sensor interfaces, data converters, filters, and power systems. Perform simulations, testing, validation, documentation, design reviews, and troubleshooting under mentorship. The role emphasizes developing foundational analog design expertise and collaborating with cross-functional engineering teams.

Provides technical support and training to customers, field applications engineers, and sales teams. Evaluates system requirements, recommends integrated circuits and reference designs, supports hardware and software evaluation platforms, reviews customer designs, diagnoses technical issues, and develops technical documentation. The role requires collaboration with internal engineering teams and hands-on debugging using laboratory equipment.

Provides senior technical leadership for inertial sensing, GNSS, navigation, and sensor-fusion applications. Supports customers with product evaluation, integration, troubleshooting, system characterization, demonstrations, and design reviews. Develops models, algorithms, simulations, software tools, technical documentation, and reference solutions. Collaborates across engineering, product, sales, software, and marketing teams, translates customer needs into product requirements, and mentors engineers. Travel is approximately 10–15%.

Lead architecture and hands-on development for dexterous robotics software and Physical AI infrastructure. Own manipulation use cases from definition through demonstration, integrating sensing, control, simulation, learned policies, data pipelines, testing, and deployment. Translate ambiguous R&D goals into delivery plans, establish production-grade reliability practices, coordinate cross-functional technical work, and mentor engineers. Develop core software in C++ and Python using ROS 2 while addressing timing, synchronization, safety, reproducibility, and performance constraints.

Develop and demonstrate AI-enabled robotic systems using robot arms, sensor-equipped hands, and application software. Integrate and troubleshoot mechanical, electrical, sensing, control, and AI components; evaluate robot behavior and sensor data; maintain reliable demonstration systems; and support customer evaluations, trade shows, and live troubleshooting. Translate customer feedback and research objectives into demonstrator requirements, test plans, and engineering improvements.

Design and operate scalable AI infrastructure across on-premises, hybrid, cloud, and edge environments. Build developer-friendly platforms for model serving, orchestration, inference optimization, resource scheduling, observability, governance, and cost efficiency. Partner with data science and ML engineering teams to create reusable architecture patterns, infrastructure-as-code frameworks, and AI developer tooling. Drive security and compliance workflows, mentor engineers, and support robotics, industrial AI, and other physical intelligence applications.