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Leads partner enablement and co-sell motions across hyperscalers, AI companies, semiconductor firms, consultancies, and data platforms. Designs joint value propositions, account-mapping plays, seller training, field activations, and internal partner playbooks. Builds relationships with partner field leaders, enables Baseten sellers, supports marketplace and deal-registration processes, and measures activation, co-sell opportunities, and influenced pipeline. Collaborates across Partnerships, Sales, FDE, Marketing, and RevOps to make partner programs repeatable and effective.

Lead a 5–10 person systems engineering team building storage and delivery products for AI inference, including tiered caching, snapshotters, filesystems, and block-level delivery. Own architecture, reliability, planning, hiring, coaching, incident response, cross-team integrations, and upstream open-source contributions. Stay hands-on with design and code reviews while optimizing storage infrastructure across heterogeneous cloud environments.

Build and lead a team of Enterprise Account Executives, recruiting and coaching sellers through complex, multi-stakeholder AI infrastructure deals. The role combines team leadership with player-coach selling, consultative AI strategy, technical evaluations, and enterprise go-to-market development. The manager will partner cross-functionally with Marketing, Product, Engineering, and Legal while shaping sales processes, territory strategy, and team culture.

Manage and mature Baseten’s security governance, risk, compliance, privacy, audit, certification, vendor assessment, and customer assurance programs. Translate regulatory and customer requirements into technical controls and product requirements, partnering with Engineering, Product, Security, Legal, and Operations. Coordinate SOC 2, ISO, HIPAA, FedRAMP, and related audits; manage risk assessments, evidence collection, remediation, security questionnaires, training, and continuous improvement in a fast-growing AI platform company.

Design and run post-training pipelines for large language models using supervised fine-tuning, reinforcement learning, reward engineering, synthetic data, and evaluation. Build customer-specific agent environments and translate production data into training signals. Analyze experiments, diagnose reward hacking and optimization instability, support deployment of models to production, publish research, and contribute to open-source training libraries.

Build and maintain revenue data models, dashboards, and documentation connecting contracts, pricing, product usage, billing, and invoices. Reconcile metered usage with billed revenue, govern revenue metrics, support forecasting and monthly close, and investigate data discrepancies. Partner with Finance, Revenue Operations, Finance Systems, Product, and Engineering to ensure reliable reporting for bookings, commitments, ARR, NDR, expansion, and consumption.

The Product Data Scientist will establish data-driven product decision-making at Baseten. Responsibilities include defining product metrics, designing measurement plans, analyzing experiments and customer behavior, forecasting, evaluating platform reliability and releases, and building trusted reporting tools. The role partners closely with Product, Engineering, founders, and GTM to guide roadmap and prioritization decisions across a technical, usage-based AI platform.

Leads and grows an inference performance engineering team focused on optimizing GPU-based AI workloads. Responsibilities include technical roadmap ownership, GPU profiling, inference runtime optimization, model and hardware bring-up, productionizing techniques such as quantization and speculative decoding, and measuring latency, utilization, and cost improvements. The manager partners across engineering and customer-facing teams, maintains high engineering standards, and remains hands-on in solving complex performance challenges.

Partner with Sales and customers to discover requirements, design AI inference solutions, lead technical demos, scope deployments, run benchmarks, and execute proofs of concept. Own repeatable deployment playbooks across language, image, video, embedding, and voice models while advising on runtime, GPU, latency, and throughput tradeoffs. Coordinate Sales, Engineering, and Forward Deployed Engineering stakeholders to deliver customer implementations.

Develop and productionize high-performance LLM inference techniques across runtimes, scheduling, serving, routing, and GPU kernels. Profile and optimize latency, throughput, memory usage, and cost; support new models and hardware; build benchmarking frameworks; and contribute to open-source inference engines such as vLLM, SGLang, and TensorRT-LLM. The role requires strong programming, GPU architecture, ML library, and LLM optimization knowledge.