Platform Engineer

Founding AI Platform Engineer | $250k | Equity | Pre Seed | Biotech | AI | Boston

Role: Founding AI Platform Engineer

Location: Boston, MA (remote role)

Salary: $200,000 - $250,000 + Equity + Benefits

An innovative technology company operating at the intersection of artificial intelligence and complex data analysis is building a next-generation platform that enables advanced reasoning over highly structured, evidence-based datasets. The organization is developing AI-driven systems, data platforms, and evaluation frameworks that support decision-making and analysis in data-intensive environments.

As an early-stage business, the company is seeking a Founding AI Platform Engineer to establish and scale the foundational engineering platform that enables both human engineers and AI-assisted development workflows to operate efficiently, reliably, and securely.

The Role

This is a unique opportunity to play a foundational role in shaping the company's engineering culture, developer platform, and AI infrastructure. You will be responsible for creating the systems, standards, and tooling that allow a small, high-performing team to develop, test, evaluate, and deploy AI-enabled products at scale.

The role blends platform engineering, developer experience, cloud infrastructure, and AI tooling. While the position requires the ability to build internal applications, APIs, dashboards, and demonstration environments, the primary focus is on creating scalable engineering foundations that support rapid innovation while maintaining reliability and governance.

Key Responsibilities:

Developer Platform & Engineering Experience

• Own and improve the developer experience for both software engineers and AI-assisted development workflows.
• Build and maintain systems for reproducible development environments, cloud workspaces, debugging, logging, tracing, and operational visibility.
• Design workflows that make AI-generated code and agent activity traceable, reviewable, and auditable.
• Establish engineering standards and best practices across the organization.

AI Development Infrastructure

• Develop and maintain internal platforms supporting AI-assisted software development and autonomous agent workflows.
• Create systems for agent execution, artifact management, state persistence, output evaluation, regression detection, and quality control.
• Implement tooling that enables reliable experimentation, testing, and comparison of AI-generated outputs.
• Support scalable evaluation processes for AI models, workflows, and automated systems.

Cloud & Platform Engineering

• Own cloud-based infrastructure supporting data processing, benchmarking, evaluation, demonstrations, and production workloads.
• Design and maintain reliable compute, storage, monitoring, and deployment solutions.
• Implement observability, cost management, operational reliability, and infrastructure automation practices.
• Manage long-running workloads, distributed processes, and platform-level services.

Data & Platform Services

• Collaborate with data and engineering teams to build and operate large-scale evidence and knowledge repositories.
• Optimize database performance, access patterns, deployment strategies, and operational reliability.
• Support structured data systems, graph-oriented architectures, and versioned datasets.
• Enable scalable access to trusted data assets across internal platforms.

Internal Applications & Tooling

• Build lightweight APIs, dashboards, internal tooling, and demonstration environments to accelerate product development and experimentation.
• Convert prototype workflows, scripts, notebooks, and proof-of-concepts into repeatable and maintainable platform services.
• Deliver practical, scalable solutions without introducing unnecessary complexity.

Required Qualifications

• Strong experience in Platform Engineering, Infrastructure Engineering, Developer Experience (DevEx), or related disciplines.
• Demonstrated ability to design and scale engineering platforms used by developers, data teams, and automated systems.
• Experience building reusable internal tools and developer productivity solutions.
• Strong understanding of software engineering best practices including testing, observability, deployment, version control, and code review processes.
• Experience with cloud platforms and modern infrastructure technologies.
• Strong proficiency in Python and modern backend development practices.
• Experience with containers, orchestration platforms, deployment automation, and CI/CD pipelines.
• Ability to balance rapid prototyping with long-term platform sustainability.
• Excellent communication and collaboration skills.

Preferred Experience

• Backend, infrastructure, platform, developer tooling, or full-stack engineering in production environments.
• Python, cloud-native architectures, containerization, workload orchestration, and infrastructure automation.
• Databases, knowledge repositories, graph-based systems, storage platforms, migrations, and version-controlled data assets.
• AI development tools, agent frameworks, prompt management, structured outputs, evaluation systems, and automated testing workflows.
• Continuous integration, observability platforms, infrastructure-as-code, security practices, secrets management, and cloud cost optimization.
• React, TypeScript, FastAPI, or similar technologies used to build internal tools, dashboards, APIs, and proof-of-concept applications.
• Experience working in highly regulated, scientific, analytical, financial, healthcare, or other business-critical environments.

What We're Looking For

The ideal candidate combines strong platform engineering expertise with a pragmatic approach to building systems that accelerate innovation. They understand how to create reliable engineering foundations while maintaining the flexibility required in a fast-moving environment.

Successful candidates will:

• Create developer experiences that improve productivity and reduce operational friction.
• Build platforms that support both human and AI-assisted development.
• Establish scalable engineering standards without introducing excessive process.
• Enable reproducible benchmarking, experimentation, and evaluation workflows.
• Deliver reliable infrastructure that supports rapid product development and demonstration.
• Balance architectural discipline with the agility required in an early-stage company.

Success in the Role

Success will be measured by the creation of a stable, scalable engineering foundation that enables teams to move quickly and confidently. New team members should be able to onboard efficiently, development workflows should be reproducible and observable, AI-assisted changes should be reviewable and traceable, and platform services should enable experimentation, benchmarking, and product delivery with minimal operational overhead.

This is an opportunity for an experienced Platform Engineer to have a significant impact on the technical direction, engineering culture, and long-term scalability of a growing AI-focused organization.

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