Location: This is a hybrid remote/in-office role.
Medidata follows a hybrid office policy in which employees who are hired for an in-person position are expected to work on site a certain number of days per week following Company policy. The Role: You will define the technical direction for Medidata's Platform engineering organization - the systems, infrastructure, and practices that power clinical trial operations for the world's largest pharmaceutical companies. You set architectural direction across multiple teams and time horizons, translate business strategy into durable technical capabilities, and develop the Staff and Senior Staff engineers who execute within that direction.
The platform is in the middle of a fundamental shift. AI and agentic systems are moving from experimental features to core infrastructure. You will be the architectural authority for how this transformation plays out - not within a single team, but across every team that builds on the platform. You will determine where AI agents operate in production workflows and where humans must remain, define the cost, quality, and reliability trade-offs at scale, and establish the engineering practices and standards required to build and operate AI-native systems responsibly.
What You'll Do: Architectural Direction & Technical Strategy - Define the long-term technical vision for the platform - service architecture, data strategy, infrastructure evolution, and AI integration across all platform teams
- Own architectural trade-offs that span multiple systems, teams, and time horizons - balancing velocity, cost, reliability, risk, and long-term sustainability. This includes explicit investment decisions on AI adoption: model selection, build-vs-buy for AI infrastructure, and ROI frameworks for agentic automation
- Translate business strategy and product direction into technical capabilities that are durable, not reactive
- Evaluate, validate, and institutionalize new technologies and practices. You decide what enters the platform's technical foundation and what doesn't
- Define quality bars, architectural guardrails, production excellence standards, and engineering talent expectations at the organizational level
AI & Agentic Systems - Platform-Wide - Own the architectural vision for how AI and agentic capabilities are embedded across the platform - as a fundamental layer of how every team builds and operates, not a standalone initiative
- Define the organizational framework for agent autonomy: where AI agents operate with full autonomy, where human oversight is required, and how those boundaries evolve as systems mature - spanning production workflows, development processes, and operational tooling
- Shape the platform's observability and production readiness standards for AI-powered systems - cost telemetry, dual-pipeline tracing, failure mode classification, fallback mechanisms, and incident response patterns specific to agentic workloads
- Drive the architectural standards that make platform systems AI-ready: well-documented APIs, deterministic interfaces, observable behavior, and safe patterns for automated interaction
SDLC Transformation & Engineering Practice - Define and institutionalize agentic development as an engineering practice across the organization - not just tool adoption, but how software is designed, reviewed, tested, and deployed when AI agents are part of the workflow
- Establish measurement frameworks for engineering transformation: developer throughput, cost per automated decision, quality impact, and where AI augmentation creates value versus risk
- Own the architectural direction for CI/CD evolution - intelligent pipelines, AI-assisted code review, automated testing, and deployment automation
- Challenge and reshape engineering processes that don't survive the shift to AI-augmented development
Technical Leadership - Develop technical leaders - Staff and Senior Staff engineers are your primary mentorship scope. Shape how they think about architecture, trade-offs, and organizational impact
- Represent the platform's technical direction to executive leadership, product, architecture, and external stakeholders. Create narratives that connect technology decisions to business outcomes. Influence across organizational boundaries without positional authority
- Shape engineering culture and decision-making frameworks that allow teams to reason through ambiguous technical decisions without escalating to you
Requirements: - 15+ years of software engineering experience, with significant time defining architectural direction across multiple teams and systems
- 3+ years in a Principal Engineer, Distinguished Engineer, or equivalent architectural leadership role
- Proven track record translating business strategy into durable technical capabilities - defining what should be built and why, not just executing what was asked for
- Deep architectural expertise across backend systems, data infrastructure, cloud platforms (AWS strongly preferred), and distributed systems at scale
- Direct experience with AI/ML systems in production - hands-on understanding of LLM integration patterns, agentic systems, and the architectural implications of embedding AI into platform infrastructure
- Demonstrated experience driving agentic development practices - you've formed strong, experience-based opinions on how AI tools transform software engineering workflows. You can define adoption strategy for an organization, not just use the tools yourself
- Track record of developing technical leaders at the Staff/Sr Staff level
- Ability to communicate strategy, trade-offs, and risk to executives and engineering teams, and to drive alignment across teams with competing priorities
Strongly Preferred: - Experience with agent orchestration patterns, autonomy frameworks, and the architectural decisions around where AI agents should and shouldn't operate in production
- Production experience in regulated industries (life sciences, healthcare, fintech) - understanding of compliance, audit, and data integrity constraints
- Experience with SDLC transformation at organizational scale - reshaping how teams build, test, and deploy software
- Background spanning multiple technical domains (backend, data, infrastructure, AI) rather than deep specialization in one
Nice to Have: - Experience with clinical trial operations, life sciences, or regulated SaaS platforms
- External technical influence - conference talks, open-source contributions, published architectural thinking, or industry working groups
As with all roles, Medidata sets ranges based on a number of factors including function, level, candidate expertise and experience, and geographic location.
The salary range for positions that will be physically based in the NYC Metro Area is $184,500-246,000
The salary range for positions that will be physically based in the California Bay Area is $194,250-259,000.
The salary range for positions that will be physically based in the Boston Metro Area is $181,500-242,000.
The salary range for positions that will be physically based in Texas or Ohio is $162,000-216,000.
The salary range for positions that will be physically based in all other locations within the United States is $165,000-220,000.
Base pay is one part of the Total Rewards that Medidata provides to compensate and recognize employees for their work. Most sales positions are eligible for a commission on the terms of applicable plan documents, and many of Medidata's non-sales positions are eligible for annual bonuses. Medidata believes that benefits should connect you to the support you need when it matters most and provides best-in-class benefits, including medical, dental, life and disability insurance; 401(k) matching; unlimited paid time off; and 10 paid holidays per year.
Note: Please be on the lookout for job scams. Medidata recruiters will never ask applicants for monetary compensation, credit card, or banking details.
Applications will be accepted on an ongoing basis until the position is filled.
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