Key responsibilities include:

• Leading the design, implementation, benchmarking, and maintenance of core compute kernels optimized for various hardware backends (GPU, accelerators)
• Driving the performance roadmap for kernel-level improvements: vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, auto-tuning, etc.
• Integrating kernel optimizations with higher-level ML systems
• Building and maintaining profiling, instrumentation, and verification tooling to detect correctness, performance regressions, numerical issues, and hardware utilization gaps
• Leading performance investigations and root-cause analysis on inference bottlenecks, e.g. memory bandwidth, cache contention, kernel launch overhead, tensor fragmentation
• Establishing coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend portability, and maintainability
• Influencing system architecture decisions to make kernel improvements more effective (e.g. memory layout, dataflow scheduling, kernel fusion boundaries)
• Mentoring and guiding other engineers working on lower-level performance, providing code reviews, and helping set best practices
• Collaborating with infrastructure, tooling, and ML teams to roll out kernel-level optimizations into production, and monitoring their impact

Requirements include:

• BS/MS/PhD in Computer Science, or a related field
• Deep hands-on experience writing and tuning compute kernels (CUDA, Triton, OpenCL, LLVM IR, assembly or similar sort) for ML workloads
• Strong knowledge of GPU/accelerator architecture: warp structure, memory hierarchy (global, shared, register, L1/L2 caches), tensor cores, scheduling, SM occupancy, etc.
• Experience with advanced optimization techniques: tiling, blocking, software pipelining, vectorization, fusion, loop transformations, auto-tuning
• Familiarity with ML-specific kernel libraries (cuBLAS, cuDNN, CUTLASS, oneDNN, etc.) or open kernels
• Strong debugging and profiling skills (Nsight, NVProf, perf, vtune, custom instrumentation)
• Experience reasoning about numerical stability, mixed precision, quantization, and error propagation
• Experience in integrating optimized kernels into real-world ML inference systems; exposure to distributed inference pipelines, memory management, and runtime systems
• Experience building high-performance products leveraging GPU acceleration
• Excellent communication and leadership skills , able to drive design discussions, mentor colleagues, and make trade-offs visible
• A track record of shipping performance-critical, high-quality production software
• Bonus: published in systems/ML performance venues (e.g. MLSys, ASPLOS, ISCA, PPoPP), experience with custom accelerators or FPGA, experience with sparsity or model compression techniques

The pay range for this role is $190,900-$232,800 USD per year, depending on location and experience.

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As a Modeling & Simulation Engineer, you will be responsible for improving and adding to our sensor and communications model suite so that our operator training and internal engineering pipelines have a seamless translation from sim to real results.

Responsibilities

• Develop and enhance radar sensor models for use in simulation and evaluation of aeronautical vehicles.
• Translate theoretical models into efficient, reliable C++ implementations with a focus on numerical accuracy and performance.
• Validate models against real-world data and authoritative references, including field test data and calibration procedures.
• Collaborate with simulation and training application teams to ensure models integrate cleanly into operator-facing tools.
• Design automated validation and regression testing strategies for mathematical models to ensure fidelity across releases.
• Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning–based surrogates) to push the state of the art.
• Document assumptions, equations, and validation results so that both engineers and operators can trust model outputs.

Requirements

• BS or higher in Aerospace Engineering, Applied Math, Physics, or related field with 5+ years of aerospace modeling experience.
• C++ foundation with experience implementing numerical methods.
• Demonstrated experience with aerospace models such as: Radar sensors, Radio communications systems
• Experience validating simulations against real-world or experimental data.
• Ability to write clear documentation explaining assumptions, limitations, and expected behaviors of models.

Preferences

• 1+ years of experience working on pilot/operator training systems.
• Experience with Eigen or SciPy for model prototyping and validation.
• Familiarity with state estimation sensor models (GPS, IMU, Gyro, etc) for simulation environments.
• Demonstrated experience with payload sensor models including: Laser sensors, IR and optical cameras
• Knowledge of uncertainty quantification and statistical analysis methods.
• Experience with parallelization or GPU acceleration for compute-heavy models.
• Strong problem-solving mindset with a collaborative and detail-oriented approach.
• Familiarity with Python for test automation and data analysis pipelines.
• Passion for aerospace and autonomous vehicle systems.

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As a key member of the support team, you will be responsible for handling escalated technical issues from on-premise enterprise clients, reproducing complex problems, and collaborating with engineering, data, and product teams to ensure swift resolution. You will report directly to the Head of Support, and play a critical role in maintaining customer satisfaction and improving our support operations.

Key Responsibilities:

• Frontline Investigation: Handle escalated tickets from enterprise clients via Intercom, focusing on on-premise infrastructure and AI-related issues (e.g., deployment, performance, integration, security).
• Root Cause Analysis: Ask the right questions to gather context, reproduce issues in test environments, and diagnose technical problems (systems, networks, storage, GPU clusters, AI models).
• Cross-Team Collaboration: Work closely with engineering, and deployment teams to escalate, track, and resolve incidents efficiently.
• Proactive Communication: Provide clear, empathetic, and timely updates to clients and internal stakeholders, ensuring transparency throughout the resolution process.

Knowledge Sharing & Process Improvement:

• Documentation: Create and update technical FAQs, troubleshooting guides, and internal knowledge base articles to empower self-serve/L1 team and reduce recurrence of issues.
• Feedback Loop: Identify recurring pain points in on-premise deployments and suggest improvements to product, documentation, or support workflows.

Customer-Centric Approach:

• Empathy & Ownership: Maintain a customer-first mindset, ensuring clients feel heard and supported, even in high-pressure situations.
• Solution-Oriented: Proactively propose workarounds, fixes, or process optimizations to enhance the customer experience and reduce incident resolution time.

Technical Expertise:

• On-Premise & Cloud Environments: Deep understanding of Linux/Windows servers, networking, virtualization, storage, security (firewalls, RGPD compliance), and cloud providers (AWS, GCP, Azure).
• Kubernetes/Helm: Experience with deployment, scaling, and troubleshooting of applications in Kubernetes clusters using Helm charts.
• Terraform: Familiarity with Infrastructure as Code (IaC) for managing cloud resources is a strong plus.
• AI Infrastructure: Knowledge of AI/ML pipelines, LLM/RAG deployments, GPU acceleration, and data storage solutions for enterprise clients.
• Tooling: Proficiency in Intercom, monitoring tools, scripting (Bash/Python), and diagnostic utilities (logs, performance metrics).

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At Synopsys, we drive the innovations that shape the way we live and connect. Our technology is central to the Era of Pervasive Intelligence, from self-driving cars to learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content.

You Are:

You are an accomplished engineering leader with over 3-6 years of experience in developing large-scale applications, particularly within the EDA domain. Your expertise spans the entire lifecycle of solution development,from initial specification to hands-on implementation, customer engagement, and iterative refinement. You thrive in environments that demand both deep technical prowess and strong leadership, and you are passionate about mentoring the next generation of engineers. Your background in C/C++ development is robust, and you approach new languages and technologies with curiosity and adaptability, making you an ideal fit for our dynamic team. You having experience with CUDA, GPU acceleration and GPU architecture knowledge is plus.

What You’ll Be Doing:

Enabling GPU Acceleration for Fusion Compiler entire R2G flow. This includes GPU acceleration of each of the engine in R2G flow with new problem formulation to take advantage of GPU architectures. These engines include placement, global routing, detail routing, CTS, optimization, timer, extraction, legalizer and synthesis.

Owning projects end-to-end,from requirements gathering and design specification to development, testing, and customer interaction,ensuring high-quality deliverables.

Collaborating closely with cross-functional teams, including product management and product engineering.

The Impact You Will Have:

Delivering GPU Accelerated Fusion Compiler, which will be game changing for chip design and implementation steps by reducing flow cycle times from weeks to days (or hours).

Empowering Synopsys customers to achieve faster turn around time and accelerating their design cycles and reducing time to market.

Elevating the technical excellence of the team by sharing best practices, fostering a culture of learning, and mentoring future leaders.

Shaping the roadmap for Digital Implementation solutions, ensuring that Synopsys remains at the forefront of EDA technology.

What You’ll Need:

Minimum 3-6 years of hands-on experience in developing software projects, preferably in EDA or semiconductor domains.

Expert proficiency in C/C++ development, with a proven track record of delivering robust, scalable solutions.

Experience with physical design, placement, and routing flows in EDA tools.

Experience with CUDA, GPU acceleration and GPU architecture knowledge is plus.

Strong knowledge of software architecture, Design Thinking, and use of design patterns.

Excellent communication skills for technical interactions.

Who You Are:

Innovative thinker who embraces new technologies and methodologies.

Strong problem solver with a strategic mindset and attention to detail.

Effective communicator, able to translate complex technical concepts for diverse audiences.

Collaborative team player, eager to contribute and learn from others.

Adaptable and resilient in the face of evolving challenges and requirements.

The Team You’ll Be A Part Of:

You’ll join the Fusion Compiler GPU Acceleration team in Synopsys Sunnyvale, CA (or Hillsboro, OR), a group of passionate engineers focused on developing industry-first and game changing GPU Accelerated Digital Implementation solution. This development is part of Nvidia/Synopsys GPU Acceleration collaboration. This team is driving innovation in EDA and empowering customers worldwide by accelerating their design cycles and reducing time to market.

Rewards and Benefits:

We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.

At Synopsys, we want talented people of every background to feel valued and supported to do their best work. Synopsys considers all applicants for employment without regard to race, color, religion, national origin, gender, sexual orientation, age, military veteran status, or disability.

In addition to the base salary, this role may be eligible for an annual bonus, equity, and other discretionary bonuses. Synopsys offers comprehensive health, wellness, and financial benefits as part of a competitive total rewards package. The actual compensation offered will be based on a number of job-related factors, including location, skills, experience, and education. Your recruiter can share more specific details on the total rewards package upon request. The base salary range for this role is across the U.S.

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As a key member of the support team, you will be responsible for handling escalated technical issues from on-premise enterprise clients, reproducing complex problems, and collaborating with engineering, data, and product teams to ensure swift resolution. You will report directly to the Head of Support, and play a critical role in maintaining customer satisfaction and improving our support operations.

This is a unique opportunity to work at the intersection of AI infrastructure, customer success, and technical problem-solving.

Key Responsibilities:

Technical Support & Incident Management

• Frontline Investigation: Handle escalated tickets from enterprise clients via Intercom, focusing on on-premise infrastructure and AI-related issues (e.g., deployment, performance, integration, security).

• Root Cause Analysis: Ask the right questions to gather context, reproduce issues in test environments, and diagnose technical problems (systems, networks, storage, GPU clusters, AI models).

• Cross-Team Collaboration: Work closely with engineering, and deployment teams to escalate, track, and resolve incidents efficiently.

• Proactive Communication: Provide clear, empathetic, and timely updates to clients and internal stakeholders, ensuring transparency throughout the resolution process.

Knowledge Sharing & Process Improvement

• Documentation: Create and update technical FAQs, troubleshooting guides, and internal knowledge base articles to empower self-serve/L1 team and reduce recurrence of issues.

• Feedback Loop: Identify recurring pain points in on-premise deployments and suggest improvements to product, documentation, or support workflows.

Customer-Centric Approach

• Empathy & Ownership: Maintain a customer-first mindset, ensuring clients feel heard and supported, even in high-pressure situations.

• Solution-Oriented: Proactively propose workarounds, fixes, or process optimizations to enhance the customer experience and reduce incident resolution time.

Technical Expertise

• On-Premise & Cloud Environments: Deep understanding of Linux/Windows servers, networking, virtualization, storage, security (firewalls, RGPD compliance), and cloud providers (AWS, GCP, Azure).

• Kubernetes/Helm: Experience with deployment, scaling, and troubleshooting of applications in Kubernetes clusters using Helm charts.

• Terraform: Familiarity with Infrastructure as Code (IaC) for managing cloud resources is a strong plus.

• AI Infrastructure: Knowledge of AI/ML pipelines, LLM/RAG deployments, GPU acceleration, and data storage solutions for enterprise clients.

• Tooling: Proficiency in Intercom, monitoring tools, scripting (Bash/Python), and diagnostic utilities (logs, performance metrics).

Who you are:

Required Experience: 3+ years in technical support, systems administration, or DevOps, with a focus on on-premise or hybrid infrastructures.

Technical Skills:

• Hands-on experience with troubleshooting complex technical issues in enterprise environments.

• Knowledge of AI/ML workflows, data pipelines, or high-performance computing (a strong plus).

• Familiarity with ticketing systems (Intercom), RGPD compliance, and security best practices.

Soft Skills:

• Exceptional problem-solving and analytical skills.

• Strong written and verbal communication in French and English (additional languages are a bonus).

• Ability to explain technical concepts clearly to non-technical stakeholders.

Mindset:

• Customer-obsessed, with a passion for delivering high-quality support.

• Collaborative, able to work effectively in a distributed, fast-paced team.

• Curious and adaptable, with a willingness to learn and master new technologies.

Why Join Mistral AI?

• Impact: Directly contribute to the success of enterprise AI deployments and shape the future of on-premise support.

• Growth: Opportunities for career advancement in support leadership, technical specialization, or customer success.

• Innovation: Work with cutting-edge AI technology in a dynamic, mission-driven company.

• Team: Join a passionate, diverse, and low-ego team that values collaboration and continuous learning.

• Work Environment: Hybrid flexibility (Paris office) with a focus on work-life balance and professional development.

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What you'll do

As a Software Developer, you will be responsible for developing, implementing, and maintaining combustion and reactive-flow models within a finite volume CFD solver environment. You will also enhance numerical algorithms to improve stability, accuracy, and computational performance, including adaptation for parallel and high performance computing environments.

What you need

• PhD in Mechanical Engineering, Chemical Engineering, Physics, Computational Engineering, or a related technical discipline
• Strong theoretical understanding of combustion, fluid mechanics, thermodynamics, and heat transfer
• Experience with finite volume CFD methods and numerical discretization techniques
• Proficiency in C++ or Fortran for scientific or engineering software development; additional experience with Python is advantageous

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