Senior Engineer, System-Level Design Verification
Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, placement and routing (P&R), static timing analysis (STA), signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and quality of results (QoR). Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, working closely with verification, extraction, timing, design for test (DFT), and EDA vendors.
Director of Customer Engineering
Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, P&R, STA, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and QoR. Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.
Research Engineer, Data Infrastructure
The role involves building and operating the next generation of data infrastructure at Mistral AI, being a core contributor to the design and scaling of massive compute fleets and storage systems for high performance and scalability. Responsibilities include architecting and maintaining multi-cluster orchestration layers for optimizing workload placement across diverse hardware and regions, designing future-proof storage systems anticipating exabyte scale growth, contributing to the internal training platform development to support model training and fine-tuning across Kubernetes and SLURM environments, implementing and managing metadata and lineage systems to provide visibility and traceability of data and model pipelines, and managing cloud-native deployments using modern workflows to ensure scalability and operational excellence. The role also includes full lifecycle ownership, from migrating away from legacy orchestrators to implementing production-grade pipelines and participating in on-call rotations for critical training jobs.
Member of Technical Staff (Applied AI Engineer)
The role involves working on custom memory systems that grow and scale as users use the platform, developing a custom cutting-edge agent, managing bare metal infrastructure and scalability with concurrency and high reliability, optimizing cost and output with multiple models, and evaluating large language model (LLM) performance across a wide domain of tasks.
AI Intern
The role involves translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms including transformers, testing, evaluating, benchmarking the quality of models, publishing models, data sets, and evaluations, deploying models in production through containerization, working with customers and internal employees to refine model quality, establishing continuous learning pipelines using online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
Applied AI Inference Engineer
Develop and maintain software systems and product features using one or more general-purpose programming languages in a production-level environment, with a preference for Python due to its relevance in ML projects. Drive customer impact by designing, implementing, and deploying Baseten solutions end-to-end, working with customers’ engineering teams at every stage of the customer journey including sales, implementation, and expansion. Deliver with velocity by turning vague objectives into clear specs and well-defined PoCs to rapidly ship well-tested services and outcomes for customers. Optimize and enhance AI/ML projects, contributing to continuous improvement of the technical stack, including developing features and PRDs with other engineering and product organizations. Own products and customer projects end-to-end, functioning as an engineer, project manager, and product manager with focus on user empathy, project specification, and execution. Navigate ambiguity and exercise good judgment on tradeoffs and tools needed to solve problems while avoiding unnecessary complexity. Demonstrate pride, ownership, and accountability for your work.
AI Solutions Engineer
As an AI Solutions Engineer at Baseten, the responsibilities include partnering directly with customers to architect, build, and deploy high-scale production AI applications on Baseten’s platform, owning the journey from initial exploration to production deployment, translating ambiguous business goals into reliable, observable services with clear quality, latency, and cost outcomes. The role requires developing and maintaining software systems and product features using general-purpose programming languages, preferably Python, in a production-level environment. It involves designing, implementing, and deploying Baseten solutions end-to-end by collaborating with customers' engineering teams throughout the sales, implementation, and expansion stages. The engineer is expected to turn vague objectives into clear specifications and well-defined proofs of concept to rapidly ship well-tested services and outcomes. They also optimize and enhance AI/ML projects, contribute to continuous improvement of the technical stack, develop features and product requirement documents with other engineering and product teams, and own products and customer projects end-to-end as both an engineer and product manager with focus on user empathy, project specification, and execution. The role requires navigating ambiguity, exercising good judgment on tradeoffs and tools, and demonstrating pride, ownership, and accountability for their work, expecting the same from teammates.
AI Productivity Engineer
The AI Productivity Engineer will take clear ownership of rapid AI adoption across the engineering organization by building AI-powered tools and systems that improve engineering productivity, reducing friction, automating repetitive tasks, and embedding intelligence into workflows. Responsibilities include identifying high-friction areas in engineering workflows, designing and building production-grade AI-powered developer tooling for coding, testing, PR reviews, and debugging, building contextual AI assistants using internal data and tools, exploring, prototyping, and productionizing AI solutions, automating workflows across platforms like GitLab, Jira, CI/CD, Slack, and observability tools, designing and operating internal AI services and orchestration layers, owning solutions end-to-end from discovery to iteration, working hands-on with engineering teams to remove friction and enable tool usage, and measuring success through adoption, impact, and tangible time saved for engineers. The role explicitly excludes building AI features for customer-facing products, speculative AI research without clear outcomes, acting as general internal support, and owning generic ML infrastructure unrelated to developer productivity.
Defense / Edge Tech Lead
As the Defense / Edge Tech Lead, you will own the technical direction for deploying Deepgram's speech-to-text (STT) and text-to-speech (TTS) models to edge and embedded environments. Your responsibilities include leading the technical strategy for edge deployment, defining the architecture for on-device, on-premises, and air-gapped inference across diverse hardware targets. You will optimize models for edge and embedded platforms through quantization, pruning, distillation, and runtime optimization to meet latency, memory, and power constraints. You will partner with hardware vendors like Qualcomm and Motorola for SDK integration, performance benchmarking, and joint go-to-market efforts. Supporting defense customer requirements through AWS NatSec partnerships by translating mission requirements into engineering deliverables is also part of your role. You will design and build edge runtime infrastructure such as model packaging, deployment pipelines, OTA update mechanisms, and telemetry for devices in low or no connectivity environments. Deployments must be hardened for security-sensitive environments with features like secure boot chains, encrypted model storage, tamper detection, and audit logging. You will benchmark and validate performance across hardware platforms, establishing test suites for latency, accuracy, power consumption, and resource utilization. Collaboration with Research and Engine teams to influence model architectures toward edge-friendly designs is expected. Furthermore, you provide technical leadership to cross-functional teams on defense and edge projects, set engineering standards, review designs, and mentor engineers on systems and optimization practices.
SDET II
Testing of AI based conversational products; Monitoring and improving quality assurance process ensuring any agreed-upon standards and procedures are followed; Providing a high level of data quality awareness across multiple teams; Evaluating and identifying where enhancements in accuracy of models are required; Detailed testing feedback preparation to help the team to improve AI models.
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