Principal Software Engineer, Data Products

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Software Engineer, Data Products based in United States. This role provides senior technical leadership for data-intensive systems that power logistics decisions at scale. You will set engineering standards across architecture, design, testing, observability, security, reliability, and operational readiness. Working closely with software engineers, data scientists, and data engineers, you will turn analytical and prototype work into robust production systems. The role also sits at the intersection of application engineering, data platforms, and AI/ML, with significant influence over how these capabilities are integrated into customer-facing products. You will drive technical direction across teams through hands-on engineering, RFCs, design reviews, mentoring, and multi-quarter roadmaps. As a principal-level individual contributor, you will help establish reusable engineering practices while enabling teams to operate with greater autonomy and confidence.

Set and demonstrate a high engineering standard through architecture, design, and code reviews, while raising expectations for testing, observability, security, reliability, and operational readiness across data-focused engineering teams. Design and build the interfaces between application services, data systems, and ML systems, providing hands-on reference implementations rather than limiting contributions to architectural documentation. Author and lead technical RFCs covering data, API, and application architecture, facilitating design reviews and ensuring technical decisions are effectively adopted. Establish performance, security, reliability, and operational standards for application services that interact with data and machine learning systems. Work directly with data scientists and data engineers to identify workflow friction and turn analytical or prototype work into secure, maintainable, scalable, and production-ready systems. Ensure data products deliver meaningful customer and business outcomes while making pragmatic trade-offs between speed to market, technical quality, architecture, and long-term maintainability. Advise on ML development workflows spanning experimentation, training, tracking, model registration, validation, deployment, monitoring, and retraining. Explore and develop agentic AI workflows, evaluating them against real-world traffic and measurable outcomes, strengthening solutions that demonstrate value and discontinuing those that do not. Identify repeated engineering patterns and turn them into reusable primitives, standards, and documentation that enable teams to solve similar problems independently. Mentor and develop engineers and emerging technical leaders through pairing, coaching, knowledge sharing, and progressive ownership transitions. Drive technical direction across multiple teams and deliver complex, multi-quarter initiatives without relying on direct reporting authority. Promote adoption of effective standards and tooling by demonstrating their value through practical engineering outcomes and improved developer productivity. Requirements: 10+ years of software engineering experience, with substantial ownership of large-scale production systems and a demonstrated history of establishing engineering standards. Proven experience operating at a principal-level individual contributor capacity, including setting technical direction across teams, authoring RFCs, leading design reviews, and delivering multi-quarter technical roadmaps without direct authority. Deep experience with distributed, data-intensive production systems, including architectural decisions driven by latency, throughput, availability, reliability, and cost. Extensive Kubernetes expertise, including building and deploying containerized production services and working with autoscaling, resource constraints, resilience patterns, and deployment standards for ML workloads. Strong experience collaborating with data scientists and data engineers and transforming analytical models, prototypes, or experimental work into production-grade systems. Strong understanding of engineering practices for scalable, reliable, secure, and maintainable data products. Demonstrated ability to build standards and tooling that engineering teams voluntarily adopt because they improve effectiveness and technical outcomes. Proven mentorship and technical leadership skills, with the ability to develop engineers and cultivate the next generation of technical leaders. Strong communication and collaboration skills, including the ability to influence architecture and engineering decisions across organizational boundaries. Solid understanding of effective ML development workflows and the ability to advise teams building production ML capabilities. Experience building agentic AI solutions for customers, including evaluation frameworks, guardrails, and observability, is a plus. Hands-on experience with Databricks and its ML tooling is a plus. Experience with feature-store patterns and online/offline consistency is a plus. Ability to work effectively in a remote-first, globally distributed environment. Benefits: Remote position in the United States, with location eligibility subject to applicable geographic restrictions. Base salary range of $212,000–$287,000 annually, with offers generally anchored around the midpoint of the range and adjusted based on experience, skills, business needs, market value, and other relevant factors. Two U.S. compensation ranges based on geographic labor-market differences, including a higher range for certain higher-cost locations such as New York City and California. Equity as part of the total compensation package. Medical, dental, and vision coverage, with 90% of premiums covered by the company, including dependent coverage. Optional pet insurance. Flexible working hours and a take-as-much-as-needed vacation policy. One week-long company-wide winter slowdown. Three Volunteer Days Off (VTOs). Work-from-home stipend to support a home office setup. Charity donation matching of up to $100. Professional and career development programs, coaching, tools, resources, and an individual learning stipend. Opportunities for in-person team and company gatherings through a distributed-team program, including regular off-sites and local events. Inclusive hiring practices and reasonable accommodation support during the application and recruitment process.

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