Role Description At EnCharge AI, we are building the next generation of AI compute silicon — purpose-built for high-performance, low-power, and scalable AI inference. As an Emulation Engineer, you will play a critical role in validating complex AI accelerator architectures on emulation platforms before tape-out. This position is ideal for someone passionate about bridging the gap between hardware and software in fast-paced, deep tech environments. - Set up and maintain Siemens Veloce emulation and prototyping platforms - Adapt SoC designs for Emulation and Prototyping - Develop and debug emulation testbenches and system-level environments - Support pre-silicon validation, power/performance analysis, and early software bring-up. Participate in silicon bring-up and validation. - Collaborate with design and verification teams to isolate design issues and accelerate debug. - Optimize performance of the emulation workloads and reduce turnaround time. - Work with firmware/software teams to enable use of emulators for OS and driver testing. Qualifications - BS/MS/Ph.D. in EE, CS, or related field with 7+ years of SoC design experience. - Experience with emulation platforms (Veloce, Palladium, or ZeBu) and FPGA-based prototyping systems (proFPGA, HAPS, or Protium) - Experience with emulating high speed I/O interfaces such as PCIe and UCIe and memory technologies such as LPDDR and HBM - Solid understanding of digital design, RTL (Verilog/SystemVerilog), and SoC architecture. - Proficiency in hardware debug tools, waveform viewers, and logic analyzers - Scripting skills (e.g., Python, Tcl, Perl) for automation and infrastructure development - Familiarity with UVM, simulation, and testbench environments. SystemVerilog and UVM-based verification experience a plus - Hand-on software development experience with C/C++ is a plus

Role Description We are seeking a talented Site Reliability Engineer (SRE) with a strong networking background to join the Fabric team. This role is pivotal in building and maintaining the robust infrastructure necessary for secure and efficient communication between our services. As an SRE on the Fabric team, you will leverage your expertise in networking, distributed systems, and automation to ensure our systems are resilient, scalable, and reliable. Qualifications - 10+ years of experience working on software and operating distributed systems. - Deep expertise in networking fundamentals and a good understanding of how the internet works (e.g., TCP/IP, DNS, TLS/mTLS, BGP, tunnels, overlays, and SDN principles). - Possess a customer-focused mindset, driving improvements that benefit end-users. - Value efficiency in processes and operations, with a strong preference for automation over manual processes. - Familiar with modern cloud-based infrastructure and the network design primitives of at least one of AWS, Azure, or GCP (e.g., VPCs, subnetting, routing, VPNs, peering, private link/private service connect, and CDNs). - Strong knowledge of service mesh and load-balancing concepts, eager to implement these in a multi-cloud environment. Requirements - Participate in the development of a reliable and resilient multi-cloud globally-connected network that is crucial for MongoDB’s services. - Collaborate with service-owning teams to provide internal support, addressing technical issues and offering guidance on best practices for service-to-service connectivity. - Participate in a 24/7 on-call rotation to swiftly resolve issues related to network architecture and service-to-service connectivity, ensuring minimal disruption and high availability. Benefits - Equity participation. - Participation in the employee stock purchase program. - Flexible paid time off. - 20 weeks fully-paid gender-neutral parental leave. - Fertility and adoption assistance. - 401(k) plan. - Mental health counseling. - Access to transgender-inclusive health insurance coverage. - Health benefits offerings.

FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent. By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace. We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results. Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits. What to expect on your journey with us: - A solid and innovative company with a strong market presence - A dynamic, diverse, and multicultural work environment - Leaders with deep market knowledge and strategic vision - Continuous learning and development This position leads the deployment, implementation, and optimization of machine learning pipelines to solve complex business challenges. The role involves both hands-on work and supervising a team to deliver effective machine learning engineering solutions for a line of business. The position applies in-depth knowledge of policies, procedures, and business objectives to make decisions and guide team. Performs work independently while receiving limited guidance Key Responsibilities - Delivers machine learning ops engineering tasks such as deployment, implementation, optimization, and maintenance of machine learning pipelines and models. - Ensures pipelines support efficient data ingestion, preprocessing, model training, validation, deployment and monitoring. - Implements scalable and robust machine learning solutions that can handle large volumes of data and complex models. - Implements real-time inference with high availability and low latency. - Creates strategic plans within span of control and implements them across one to two business domains. - Ensures seamless integration of pipelines with continuous integration and continuous deployment (CI/CD) tools and workflows. - Supporting and maintaining solutions in production (fixing bugs, make changes as required, maintaining models)   - Collaborates with cross-functional teams to integrate machine learning and business logic-based solutions into production systems - Effectively communicates and applies machine learning engineering value, concepts, and strategies in various scenarios with stakeholders - Recruits, hires, and mentors' top talent to build a high-performing MLOps team. Supervises, coaches, and guides direct reports - Uses advanced knowledge of code management principles to follow architectural and governance guidelines

FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent. By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace. We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results. Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits. What to expect on your journey with us: - A solid and innovative company with a strong market presence - A dynamic, diverse, and multicultural work environment - Leaders with deep market knowledge and strategic vision - Continuous learning and development This position leads the deployment, implementation, and optimization of machine learning pipelines to solve complex business challenges. The role involves both hands-on work and supervising a team to deliver effective machine learning engineering solutions for a line of business. The position applies in-depth knowledge of policies, procedures, and business objectives to make decisions and guide team. Performs work independently while receiving limited guidance Key Responsibilities - Delivers machine learning ops engineering tasks such as deployment, implementation, optimization, and maintenance of machine learning pipelines and models. - Ensures pipelines support efficient data ingestion, preprocessing, model training, validation, deployment and monitoring. - Implements scalable and robust machine learning solutions that can handle large volumes of data and complex models. - Implements real-time inference with high availability and low latency. - Creates strategic plans within span of control and implements them across one to two business domains. - Ensures seamless integration of pipelines with continuous integration and continuous deployment (CI/CD) tools and workflows. - Supporting and maintaining solutions in production (fixing bugs, make changes as required, maintaining models)   - Collaborates with cross-functional teams to integrate machine learning and business logic-based solutions into production systems - Effectively communicates and applies machine learning engineering value, concepts, and strategies in various scenarios with stakeholders - Recruits, hires, and mentors' top talent to build a high-performing MLOps team. Supervises, coaches, and guides direct reports - Uses advanced knowledge of code management principles to follow architectural and governance guidelines