VP, Software Engineering

Vice President, Software Engineering — Seven Bridges

Location: Remote. US preferred. India or EMEA candidates considered.

Reports to: Chief Technology Officer

Who are we?

Velsera is the precision engine company. We build the software researchers, scientists, and clinicians use to drive precision R&D and bring analytics closer to the point of care. Seven Bridges, Pierian, and UgenTec came together to form Velsera; we are headquartered in Boston, MA, with [current headcount] colleagues across [current country count] countries.

The Seven Bridges Platform is our research genomics platform — roughly 130 containerized services, multi-cloud, running in commercial and FedRAMP Moderate environments, where scientists run CWL, WDL, and Nextflow workflows over petabyte-scale genomic data. Current priorities are federated data management, compliance as a product capability, and governed AI.

Reporting line and scope

You report to the CTO and own engineering for the Seven Bridges Platform: architecture, delivery, and production operations, including reliability, cost, technical debt, and observability. The team is primarily in India, with a smaller group in Serbia; you lead across both sites and work daily with stakeholders in other time zones. You hold architecture authority for the platform, co-own boundary decisions with security, federal compliance, and quality leadership, and partner with a cross-business AI Engineering team.

The role is roughly 40% hands-on technical work and 60% people leadership and organizational delivery. Hands-on means owning architecture decisions of record, leading design reviews, reviewing code and infrastructure changes, and prototyping where needed. You should be credible to your most senior engineers, with a clear view of what this class of platform should become and the ability to take a team and a business there.

What will you do?

• Own the technical vision for the platform over the next three years, and turn it into an architecture and roadmap the team and the business believe in.

• Own architecture decisions of record, design reviews, multi-tenant isolation, and complexity reduction across the service estate.

• Keep delivery governed and modern: infrastructure-as-code under CI with policy-as-code, short-lived credentials, progressive rollout, and change evidence that holds up to audit in a federally authorized environment.

• Own reliability: SLOs and error budgets on the paths customers depend on, on-call design that works across two sites, and turning repeat incidents into architectural change.

• Ship governed AI in the product and apply AI to how the team builds, both under a governance policy co-owned with Risk & Compliance, and both measured.

• Build the team: management depth you can trust with delivery, the specialist skills the roadmap needs, and maintaining a thriving team through a period of growth.

What do you bring to the table?

Must have

1. 12+ years in software engineering, including several years leading multi-team organizations through managers.

1. Distributed-systems and platform architecture ownership at scale — multi-tenant isolation, service decomposition, and the judgment to reduce complexity.

1. Domain depth in scientific or biomedical data platforms — genomics, bioinformatics, life sciences, or comparable data-intensive, workflow-driven scientific computing.

1. Delivery inside a regulatory boundary such as FedRAMP, HIPAA, ISO 13485, or GxP.

1. Reliability and modern delivery engineering: SLOs, error budgets, infrastructure-as-code, CI/CD with policy-as-code and progressive rollout.

1. Production AI/LLM capability shipped under a compliance regime, or AI-assisted engineering you landed, governed and measured — beyond pilots.

1. A track record of setting technical vision for a platform and carrying an organization there.

1. Bachelor’s degree in a relevant field, or equivalent practical experience.

Nice to have

• CWL/WDL/Nextflow, GA4GH standards (DRS/WES), FHIR or OMOP.

• Federated or cross-institutional data access at scale — bringing analysis to data held by someone else.

• Federal program delivery and artifact literacy — authorization packages, significant-change requests, and corrective-action plans.

• Architecture depth in inference topology: hosting trade-offs, retrieval that inherits per-tenant authorization, evaluation harnesses, and guardrails.

• Shipping reusable product capability out of grant- or program-funded work.

Competences

Vision

The ability to step back from the daily routine, explore ideas for the future, and see the platform in a broader context and in the longer term.

• articulates where the platform must be in three years, and why

• combines scientific, regulatory, and technology trends into one coherent direction

• spots opportunities for the platform early and acts on them

Technical Depth

Personal command of the technology the platform is built on, enough to shape and challenge its biggest decisions.

• reads code, designs, and test results first-hand rather than relying on summaries

• changes a team’s technical direction with evidence rather than authority

• is treated as a peer by senior engineers in design review

Managing

The ability to lead and develop others so they perform at their best — setting direction, providing the means, and holding the bar.

• builds a management layer capable of running delivery without them, then holds it accountable

• makes hiring and performance decisions promptly and explains them plainly

• builds confidence and momentum and creates a thriving team environment

Focus on Quality

Setting high standards and treating quality, security, and compliance as design constraints rather than gates to be cleared.

• designs for auditability, traceability, and reproducibility from the outset

• converts recurring incidents into architectural and process change

• declines shortcuts that would trade a compliance position for a delivery date

Decision Quality Under Ambiguity

The ability to make timely, defensible decisions on incomplete information, and to revisit them when the evidence changes.

• commits to a sequence when the data is partial, and states what would change their mind

• separates reversible from irreversible decisions and spends attention accordingly

• records the reasoning as well as the outcome