Technical Product Manager (Systems & ML)

Company Description

Skyward Wildfire Technologies combines advanced atmospheric modeling, large-scale data, and operational systems to predict and prevent lightning-caused wildfires before they start. As we scale, the complexity of our technical dependencies requires a dedicated professional to turn ambitious scientific research into a predictable, high-velocity production roadmap.

We are seeking a Technical Product Manager with a deep engineering background to serve as the operational partner to our CTO and the CEO. You will own the execution framework, ensuring that our technical milestones are visible, sequenced, and aligned with our enterprise business objectives.

Key Responsibilities

1. Execution & Visibility (The \"Progress Signal\")

• Dashboard Ownership: Establish and maintain high-fidelity progress tracking and dashboards to provide the CEO and Board with a clear signal on bi-weekly and quarterly development velocity.
• Upstream Requirement Definition: work with the Business Development team to understand customer needs and translate those needs to technical requirements.
• Business Development Liaison: collaborate with the data analytics teams to translate technical product performance into customer facing metrics and reports in support of Business Development activities.
• Flight Operations Development Support: coordinate with the Flight Operations Engineering teams to increase awareness of project schedules and deliverables for flight operations readiness and technical performance in the field.

2. Stakeholder & System Partnership

• Executive Liaison: Serve as a trusted advisor to the CEO, providing objective data on technical tradeoffs, risks, and timelines to support sales and go-to-market strategies.
• Architecture Alignment: Collaborate with engineers to ensure our cloud-native architectures (AWS/GCP) are robust enough to support the large-scale data sets required for our models.
• Infrastructure Strategy: Support the development of our ML service cloud, ensuring it meets the scalability needs of enterprise customers.

3. System Orchestration

• Dependency Management: Manage the complex lifecycle of interconnected models, ensuring that changes in data ingestion or model architecture are communicated and integrated across the stack.
• Infrastructure Alignment: Collaborate with the engineering team to ensure cloud-native architectures (AWS/GCP) and MLOps tools are optimized for the scale of our data sets.
• Resource Allocation: Help leadership make informed decisions on technical tradeoffs, sequencing technical \"bets\" to ensure development momentum remains high.
• Sprint Rigor: Lead the day-to-day engineering rituals, ensuring work tickets are well-defined, maintained, and not siloed in disconnected message threads.
• Dependency Orchestration: Manage the critical path across data engineering, ML research, and cloud infrastructure, ensuring no team is blocked by upstream changes.

Required Experience & Qualifications

• Engineering Foundation: 10+ years of experience in technical roles, with a strong background in software engineering, systems architecture, or data science.
• The Startup-Enterprise Hybrid: Experience navigating the ambiguity of an early-stage startup (0→1) while maintaining the organizational standards of an enterprise environment.
• Data & ML Proficiency: Proven experience managing programs involving large-scale data ingestion, complex ML pipelines, and cloud infrastructure.
• Technical Communication: Exceptional ability to distill highly technical \"science\" updates into clear, business-focused progress reports for stakeholders and the Board.
• Relevant Domain Exposure: Experience with large data-sets collected via sensor systems, and operationally critical \"real-world\" software is highly preferred.

What Success Looks Like

• Transparency: The CEO, CTO and leadership have a real-time, accurate view of engineering velocity and project status without manual intervention.
• Velocity: The scientific team is unburdened from project management tasks, allowing them to accelerate the development of core algorithms and ML models.
• Alignment: Engineering teams are clear on their priorities, tickets are well-maintained, and the \"silo effect\" is eliminated.
• Scalability: Skyward moves from experimental models to a robust, scalable system where technical complexity is managed, not ignored.

Why This Role Matters

At Skyward, our success depends on the synergy between scientific innovation and engineering execution. You will be the partner who ensures our most ambitious technical projects result in a coherent, shippable product, providing the operational backbone that allows our scientific experts to thrive.

Location: Remote / Hybrid (as applicable)

Salary Band: $100,000 - $160,000