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Right-Hand Cybersecurity is building Human Risk Management solutions for enterprises. The company helps organizations understand and reduce workforce-related cybersecurity risk through intelligent security awareness, phishing simulations, AI-powered agents, behavioral insights, and risk analytics.
You will be a Product Engineer—a full-stack builder who owns features from early discovery through production release and iteration. This is not a specialist role; you will be equally comfortable clarifying ambiguous requirements, challenging product decisions, designing scalable architecture, building polished experiences, and monitoring performance after launch. You will work directly with product, design, customers, and leadership to understand problems before choosing solutions.
Key responsibilities include:
- Own features end-to-end: discovery, prototyping, architecture, implementation, testing, release, and iteration
- Turn loosely defined ideas into clear product and technical plans
- Build polished customer-facing experiences across frontend and backend
- Design reliable APIs, data models, services, integrations, and reusable product foundations
- Use AI development tools to accelerate research, prototyping, implementation, testing, debugging, and documentation
- Build AI-powered product capabilities using model APIs, agents, retrieval, structured outputs, and evaluation workflows
- Evaluate AI-generated work critically; remain accountable for quality, security, and correctness
- Make thoughtful trade-offs across customer value, delivery speed, maintainability, security, performance, and cost
- Monitor features after release, learn from production behavior and customer feedback, and iterate
- Raise the engineering bar through pragmatic standards, code reviews, documentation, testing, and architectural guidance
- Contribute outside rigid role boundaries when needed to deliver the right customer outcome
Tech stack includes React, modern JavaScript/TypeScript, Ruby on Rails, Python, Node.js, PostgreSQL, MongoDB, DynamoDB, Redis, Sidekiq, AWS (ECS/Fargate, S3, CloudFront, SQS, Lambda), enterprise security and collaboration platform integrations, and large language models/generative AI services.
"AI-native" means using AI to explore codebases, test assumptions, prototype quickly, compare solutions, automate repetitive work, improve test coverage, and shorten the path from problem to validated outcome—while knowing when AI is wrong and having the engineering fundamentals to catch it.
You may be an especially strong fit if you have been an early engineer, technical founder, or product-minded engineer in a fast-moving startup; built a product or substantial feature from zero to one; can show products, open-source work, experiments, or side projects demonstrating curiosity and initiative; have experience with enterprise SaaS, cybersecurity, analytics, workflow automation, or AI-powered products; have worked directly with customers to discover problems and validate solutions; or naturally move between product thinking, user experience, architecture, and implementation.
Within your first few months, you will develop a strong understanding of customers, architecture, and product direction; independently own and ship a meaningful customer-facing improvement; demonstrate that you can turn ambiguity into a clear, practical execution plan; improve at least one part of the engineering or product-development workflow; and become a trusted partner in product and architectural decisions.
REQUIREMENTS:
- Approximately 5+ years of professional software engineering experience, or equivalent evidence of exceptional product-building ability
- Track record of independently delivering meaningful customer-facing features or products from idea to production
- Strong fundamentals in system design, data modeling, API design, databases, testing, security, performance, and distributed systems
- Experience building across modern frontend and backend systems
- Strong product judgment and genuine care for usability, simplicity, speed, and polish
- Ability to understand customer and business context—not just the technical specification
- Comfort working with ambiguity, questioning assumptions, and proposing a better path
- Experience making architectural decisions that balance immediate delivery with long-term maintainability
- Strong written and verbal communication in a remote, collaborative environment
- High ownership: identify gaps, follow through, and remain accountable after deployment
- Practical experience using AI-assisted development tools as part of daily engineering workflow
- Ability to verify AI-generated code for correctness, security, maintainability, and alignment with existing systems
