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Owns the operating system for Baseten’s Forward Deployed Engineering team. Responsibilities include managing priority-account cadences, cross-team infrastructure programs, escalations, dependencies, metrics, engineering toil reduction, onboarding, capacity planning, and customer launches. The role coordinates technical teams and leadership across ML infrastructure, inference, platform engineering, Sales, and Solutions Architecture while driving accountability, risk mitigation, and clear executive communication.

Build customer-facing enterprise products and platforms for Baseten’s AI inference infrastructure. Own features end to end, including customer discovery, design, development, launch, and iteration. Develop scalable systems supporting billing, partnerships, enterprise identity and governance, developer productivity, and agentic tooling. Collaborate closely with product, design, engineering, and go-to-market teams while prioritizing customer impact and solving ambiguous problems independently.

Own Baseten’s internal identity and authentication infrastructure, including Okta architecture, SSO, MFA, lifecycle management, and application integrations. Build automated joiner, mover, and leaver workflows across HRIS, Google Workspace, MDM, and SaaS systems using scripting and APIs. Implement access controls, device trust, compliance processes, documentation, and audit evidence. Serve as a technical escalation point for IT Support and partner with Security on least-privilege access and SOC 2 requirements.

Drive partner ecosystem marketing by shaping partner narratives, creating content (blogs, white papers, solution briefs, case studies), building enablement and sales playbooks, executing co-marketing GTM campaigns with partners, managing partner marketing calendars, and collaborating cross-functionally with product, DevRel, growth, events, and sales teams.

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.

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.