Job description
Engineering Manager
Location: Remote (US)
Department: Insights (AI/ML)
Reports to: Director of Science and Architecture
About the Role
Irth is building a new AI-driven threat and risk management platform for pipeline asset integrity. The platform brings together three capabilities that have never previously lived in one place at Irth:
A governed, cross-product data platform built on Databricks and Azure.
An AI-powered ingestion layer that normalizes, repairs, and enriches customer data without services-heavy onboarding.
A reusable analytical layer that runs industry-standard, Irth-developed, and customer-built risk models against that data.
We are looking for an Engineering Manager to lead the team building this platform.
You will own delivery and technical execution across a small, senior team spanning data architecture, data engineering, data science, application engineering, and QA. You will be accountable for what the team ships against a phased, gated delivery plan.
This is a player-coach role. You will spend a meaningful portion of each week in the codebase, reviewing designs, and participating in code reviews. We are not looking for someone who manages from a distance. We are looking for an experienced engineer who has grown into leading people and wants to continue doing both.
The work is highly regulated. Pipeline operators use these outputs to determine where to dig, what to repair, and how to justify that spending to regulators. Every calculation therefore needs to be traceable, reproducible, and defensible under audit.
If you enjoy solving problems where correctness genuinely matters, this is an opportunity to make a meaningful impact.
Key Responsibilities
1. Delivery Ownership — Primary Responsibility
Own delivery and technical execution against the phase plan, including scope commitments, sequencing, dependencies, and release readiness at each gate.
Run the team's operating rhythm, including sprint planning, stand-ups, demos, and retrospectives, with clear visibility into blockers and schedule risk.
Translate product requirements into technical workstreams and break them into achievable increments.
Hold the line on scope when delivery plans are at risk.
Manage team capacity and velocity across parallel workstreams spanning the data platform, ingestion layer, and analytical layer.
Escalate risks early and with a clear recommendation; surface milestone risk to leadership before it becomes a missed gate.
2. Hands-On Engineering
Contribute directly to the codebase, particularly on foundational and high-risk components where experienced engineering involvement can materially improve outcomes.
Review pull requests, design documents, and architecture decision records.
Establish and maintain a high standard for engineering quality and technical decision-making.
Prototype and de-risk unproven approaches, including agentic ingestion workflows and model execution patterns, before the team commits to them.
Debug production issues alongside the team rather than delegating them.
3. Technical Direction & Engineering Quality
Partner with the Data Architect to establish and enforce standards for data modeling, lineage, governance, and platform patterns.
Own engineering quality, including test coverage, CI/CD discipline, environment promotion, observability, and the definition of done.
Make and document build-versus-buy and tooling decisions, including evaluation of third-party ingestion and AI tooling.
Balance delivery pressure against technical debt and make those tradeoffs explicit.
4. Compliance, Security & Audit Readiness
Ensure risk calculations, data transformations, and model outputs are traceable and reproducible to a standard that can withstand regulatory audit.
Hold the team accountable to platform security requirements, including access control, tenant isolation, secrets management, and SOC 2-aligned controls.
Plan and schedule production-readiness activities, including load testing, penetration testing, and access-control reviews ahead of each release milestone.
5. People Leadership & Hiring
Recruit, onboard, and develop a distributed team of mid-level and senior engineers.
Conduct regular one-on-ones, set clear expectations, provide direct feedback, and own performance management and career development.
Build a team culture in which engineers challenge one another's designs and disagree productively.
Foster an environment of technical ownership, accountability, collaboration, and continuous improvement.
6. Cross-Functional Partnership
Partner daily with Product on requirements, sequencing, priorities, and scope tradeoffs.
Work with the Project Management Office on plan integrity, dependency tracking, and gate-readiness reporting.
Support customer-facing teams during pilots and previews, including technical discovery and issue triage.
Collaborate with domain subject-matter experts to ensure model behavior remains aligned with regulatory expectations.
Requirements
Qualifications
Required
8+ years of professional software or data engineering experience, including 2+ years of formally managing engineers.
A track record of remaining hands-on as a manager, with recent and demonstrable individual contributions to production systems.
Experience delivering data-intensive or ML-backed products end to end, from architecture through production operation.
Working knowledge of modern cloud data platforms; Databricks and Azure experience strongly preferred, including Delta Lake, Unity Catalog, and workflow orchestration.
Strong Python and SQL skills, with the ability to read, review, and write production code across the team's technology stack.
Experience with CI/CD, infrastructure as code, environment promotion, and release management.
Demonstrated ability to run a phased delivery plan with hard external commitments and communicate status honestly and effectively to executives.
Experience hiring, onboarding, and developing engineers in a distributed or fully remote environment.
Strong written communication skills, with the ability to author design documents, architecture decision records, and clear status narratives.
Preferred
Experience leading teams that build regulated, audit-defensible software where outputs are subject to external review.
Familiarity with MLOps practices, including model registries, versioning, deployment, monitoring, and retraining pipelines.
Experience with geospatial data and GIS-driven analytics.
Exposure to agentic AI or LLM-based document extraction in production environments, beyond prototypes.
Experience integrating third-party or customer-supplied models into a governed execution framework.
Background in multi-tenant SaaS, including per-tenant isolation and data residency requirements.
Experience using AI-assisted coding tools such as Cursor or GitHub Copilot and/or agentic coding tools such as Claude Code as part of a professional development workflow, along with a thoughtful perspective on responsible adoption across an engineering team.
Nice to Have
Understanding of pipeline integrity management concepts, including inline inspection, corrosion and crack growth, consequence modeling, and risk-based prioritization.
Familiarity with PHMSA 49 CFR 192, ASME B31.8S, or comparable regulatory frameworks.
Prior experience in energy, utilities, or critical infrastructure software.
Experience standing up a new team on a new platform rather than inheriting a mature engineering organization.
Success Metrics
Success in this role will be measured by:
On-time delivery: Phase commitments are delivered on schedule, with gate criteria met and supporting evidence produced.
High-performing team: A strong engineering team is hired, onboarded, developed, and retained, with clear ownership across the platform.
Hands-on technical contribution: Sustained personal contribution through code, code reviews, design work, and other visible technical artifacts in the repository.
Audit-ready platform: Outputs are reproducible and defensible, with no material findings in security or compliance reviews.
Predictable execution: Forecasts are accurate, risks are escalated early, and gate reviews have few surprises.
Cross-functional effectiveness: Strong working relationships are maintained with Product, the Project Management Office, customer-facing teams, and domain subject-matter experts.
Benefits
What We Offer
Join a dynamic, growing company with a strong reputation in its industry.
Competitive salary.
Comprehensive health plan options, including medical, dental, and vision coverage.
401(k) plan with company match.
Flexible PTO policy plus company-paid holidays.
Additional benefits, including life insurance, pet insurance, and employee discounts and perks programs.
Generous one-time work-from-home stipend to help you set up your home workspace.
Company events and opportunities to connect, including monthly team lunches, volunteer outings, and quarterly gatherings.
Hybrid employees have access to complimentary snacks, beverages, and coffee at our Columbus, Ohio office.
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.