Senior AI Software Engineer Jobs
Senior AI Software Engineer jobs are open across technology, finance, healthcare, and defense, from mid-level to staff and principal, with specializations in large language models, computer vision, and MLOps. Find a role that fits from the openings below and apply directly.
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Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.
Key Responsibilities
- Select and optimize AI models for autonomous applications, ensuring scalability, low latency, and real-time performance across edge and cloud deployments.
- Collaborate with cross-functional teams to ensure seamless integration of AI frameworks into software stacks, optimizing inference pipelines and model performance for real-world use cases.
- Provide technical leadership and guidance in AI software best practices, focusing on deep learning frameworks, GPU-accelerated computing, model compression, and efficient deployment strategies for autonomous systems.
- Analyze and optimize AI software stack performance, particularly in real-time inference, GPU-accelerated, and autonomous navigation environments, identifying bottlenecks and implementing targeted improvements.
- Stay updated with the latest trends and technologies in AI frameworks, foundation models, edge AI deployment and autonomous systems integration, offering insights and recommendations to continuously advance AI capabilities.
- AI Frameworks: Proven experience designing and implementing solutions using leading AI frameworks such as PyTorch, TensorFlow, JAX, or ONNX Runtime, with a focus on autonomous applications.
- AI Model Analysis & Optimization: Strong knowledge and hands-on experience with model profiling, benchmarking, quantization, pruning, and distillation techniques to optimize AI models for performance and efficiency.
- ROCm, CUDA & GPU Computing: Deep expertise in ROCm or CUDA programming, GPU kernel optimization, and GPU memory management for accelerating AI inference and training workloads.
- System Performance Analysis: Expertise in profiling and analyzing end-to-end AI system performance using tools such as NVIDIA Nsight, TensorRT, Triton Inference Server, or similar profiling and optimization platforms.
- Autonomous Systems: Experience deploying AI models within software stacks, including integration with ROS/ROS2, real-time systems, and edge AI hardware platforms (NVIDIA Jetson, etc.).
- Problem-Solving Skills: Excellent problem-solving skills and attention to detail in debugging complex AI model behavior, performance regressions, and hardware-software interactions.
- Collaboration and Communication: Ability to work collaboratively in a cross-functional team environment with strong written and verbal communication skills.
- Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field
Location
- San Jose or Austin
#LI-BW2
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.
Key Responsibilities
- Select and optimize AI models for autonomous applications, ensuring scalability, low latency, and real-time performance across edge and cloud deployments.
- Collaborate with cross-functional teams to ensure seamless integration of AI frameworks into software stacks, optimizing inference pipelines and model performance for real-world use cases.
- Provide technical leadership and guidance in AI software best practices, focusing on deep learning frameworks, GPU-accelerated computing, model compression, and efficient deployment strategies for autonomous systems.
- Analyze and optimize AI software stack performance, particularly in real-time inference, GPU-accelerated, and autonomous navigation environments, identifying bottlenecks and implementing targeted improvements.
- Stay updated with the latest trends and technologies in AI frameworks, foundation models, edge AI deployment and autonomous systems integration, offering insights and recommendations to continuously advance AI capabilities.
- AI Frameworks: Proven experience designing and implementing solutions using leading AI frameworks such as PyTorch, TensorFlow, JAX, or ONNX Runtime, with a focus on autonomous applications.
- AI Model Analysis & Optimization: Strong knowledge and hands-on experience with model profiling, benchmarking, quantization, pruning, and distillation techniques to optimize AI models for performance and efficiency.
- ROCm, CUDA & GPU Computing: Deep expertise in ROCm or CUDA programming, GPU kernel optimization, and GPU memory management for accelerating AI inference and training workloads.
- System Performance Analysis: Expertise in profiling and analyzing end-to-end AI system performance using tools such as NVIDIA Nsight, TensorRT, Triton Inference Server, or similar profiling and optimization platforms.
- Autonomous Systems: Experience deploying AI models within software stacks, including integration with ROS/ROS2, real-time systems, and edge AI hardware platforms (NVIDIA Jetson, etc.).
- Problem-Solving Skills: Excellent problem-solving skills and attention to detail in debugging complex AI model behavior, performance regressions, and hardware-software interactions.
- Collaboration and Communication: Ability to work collaboratively in a cross-functional team environment with strong written and verbal communication skills.
- Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field
Location
- San Jose or Austin
#LI-BW2
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
Senior AI Software Engineer Jobs by Experience Level
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Who's Hiring
- Google73
- JPMorganChase68
- Amazon44
- Amazon Web Services29
- Apple26
Top Industries Hiring
- Technology & Software46
- Electronics & Hardware13
- Banking & Financial Services10
- Manufacturing7
- Education5
What Employers Look For
The qualifications that appear most often in senior AI software engineer jobs.
- Advanced degree or equivalent experience in computer science, machine learning, or a related field
- Production experience designing, training, and deploying large-scale machine learning models
- Proficiency in Python and at least one deep learning framework such as PyTorch or TensorFlow
- Hands-on experience with cloud ML platforms including AWS SageMaker, Google Vertex AI, or Azure ML
- Demonstrated ability to lead technical projects and mentor junior engineers across the AI stack
- Familiarity with MLOps tooling, model monitoring, and CI/CD pipelines for ML systems
Tips for Your Senior AI Software Engineer Job Search
Quantify model performance on your resume
Hiring managers for senior AI roles scan for concrete outcomes, not just tools. Replace vague claims with metrics like latency improvements, accuracy gains, or cost reductions your models delivered in production. Generic AI buzzwords without numbers get filtered out fast.
Match your stack to the job description
Senior AI roles split sharply between PyTorch and TensorFlow shops, between cloud-native MLOps and on-prem deployments. Read each posting carefully and mirror its exact tooling language in your resume so automated screening systems surface your application correctly.
Apply early to roles that fit
Migrate Mate lists senior ai software engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings by model deployment environment
Filter openings by whether they describe edge deployment, cloud inference, or real-time serving pipelines. Your experience transfers differently across these environments, and applying to roles that match your actual deployment background lifts your interview conversion rate significantly.
Prepare a system design answer for AI infrastructure
Most senior AI interviews include a design round covering data pipelines, feature stores, or model serving at scale. Practice walking through trade-offs between batch and online inference, retraining cadence, and monitoring drift so you can answer confidently without over-rehearsing a script.
Negotiate scope before you negotiate compensation
Before discussing pay, confirm whether the role owns model research, deployment, or both, and who controls compute budgets. Scope mismatches at the senior level are a leading reason engineers leave within a year, so clarifying ownership early protects you from a poor fit.
Senior AI Software Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most senior ai software engineers?
The companies hiring the most senior ai software engineers right now include Google, JPMorganChase, and Amazon, with the largest share of openings in California, Washington, and Texas, based on current listings on Migrate Mate as of September 2026. Demand is concentrated in technology, financial services, and defense sectors, though healthcare and retail are adding senior AI roles steadily.
How many senior ai software engineer jobs are remote?
About 75% of senior ai software engineer openings are fully remote or hybrid as of September 2026, though the share varies by specialization. Roles focused on NLP, LLM fine-tuning, and recommendation systems tend to offer the most location flexibility, while positions involving proprietary hardware, on-prem infrastructure, or defense clearances are more likely to require on-site presence.
How do you become a senior ai software engineer?
You become a senior ai software engineer by building a foundation in machine learning fundamentals and software engineering, then deepening expertise in a specific domain such as NLP, computer vision, or reinforcement learning. Getting models into production is the key differentiator at the senior level, so focus on shipping end-to-end systems, owning post-deployment monitoring, and progressively taking on technical leadership within a team.
Can you get hired as a senior ai software engineer without prior senior-level experience?
You can move into a senior ai software engineer role without holding a previous senior title if you have demonstrable impact at scale. Employers look for engineers who have owned a model from research through production, led cross-functional delivery, or published work that shows independent technical judgment. A strong portfolio of shipped AI systems often carries more weight than a senior job title from a previous employer.
What does the senior ai software engineer interview process look like?
The senior ai software engineer interview process typically includes a recruiter screen, a technical phone interview covering ML fundamentals and coding, a machine learning system design round, and a full virtual or on-site loop. The loop usually combines coding exercises, a research or paper discussion, a system design session focused on AI infrastructure, and behavioral interviews assessing technical leadership and cross-team collaboration. Final rounds often include a presentation of past work.
Where can I find and apply to senior ai software engineer jobs?
You can find and apply to senior ai software engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the available roles, find the ones that match your background and target specialization, and apply directly to each listing that fits.
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