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Software Engineer, Systems Architecture

AI Summary
Valinor is seeking a full-time Software Engineer to own end-to-end systems architecture for a stealth defense technology product. The role combines edge and cloud architecture, sensor-data ingestion, time-series storage, detection and classification, and geospatial product interfaces. The engineer will work closely with a General Manager and a small engineering team while independently establishing infrastructure, CI/CD, and core technical decisions. Candidates need broad full-stack capability across Python or Go, React/TypeScript, cloud systems, and sensor or signal-processing domains. Eligibility to obtain and maintain a U.S. security clearance is required.
Remote from
USA
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
13 Dec 2026
Listing views
50
Application actions
3
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Opportunity details

About this role.

Valinor is a new type of defense and government tech company focused on solving the problems others don’t. Our unique model of centralized go-to-market and decentralized engineering allows us to move with unparalleled efficiency to identify unmet needs, build right-sized solutions, and get products where they matter most – in the hands of users. Unlike others in the space, who focus on the flashy problems, Valinor exists to solve the quiet, unaddressed problems-the small but significant ones that lead to cracks in the country’s strength, stability, and security.

Backed by General Catalyst, Founders Fund, and Friends & Family Capital, Valinor was founded in 2024 and has established strategic partnerships with Palantir, Anduril, and Helsing. We are building the dream team, and we want you on it.

About The Role

We’re looking for a software engineer to own the architecture, data handling, and product layer of the software stack at one of Valinor’s product companies, currently in stealth. The system turns raw sensor data into a usable, trusted picture for the end user. You’ll work directly with the General Manager and alongside a small team of engineers to rapidly develop a new product. You’ll also partner with product enablement at the Valinor Hub to stand up infrastructure and management.

We see this as a step-up role: someone who has already been part of building a system like this, whether embedded/edge software, sensor or data pipelines, or detection and classification work, as a strong contributor, and is ready to own that kind of architecture for the first time, with direct partnership from the GM on the highest-stakes tradeoffs.

What You’ll Do

1. Overall Software Architecture

  • Design the software architecture that ties the products data flow, processing, and user-facing product together end to end.

  • Define clean interfaces with the sensor integration and API work, so the pieces fit into a coherent whole rather than a collection of one-offs.

  • Make and document the core architecture decisions early, since the system needs to flex as we learn what the product needs to become.

2. Data Handling & Pipeline

  • Architect the edge vs. cloud processing split — what runs on-system under power/compute constraints versus what gets backhauled.

  • Stand up time-series and object storage systems for sensor and telemetry data, and the ingestion pipeline that gets data from the system to backend reliable.

  • Design for bandwidth-constrained, compression-aware data transport, and build in the data versioning and observability needed to trust what’s flowing in as sensor types multiply.

3. Detection, Classification & Pattern-of-life

  • Work with various sensor modalities, including acoustic, EO/IR, radar, RF/AIS, and others, to build detection and classification capability — starting with classical signal-processing methods where appropriate, and applied ML where it earns its complexity.

  • Contribute to pattern-of-life anomaly detection work.

  • Fuse multi-modal signals into a single detection/classification output.

4. User Interface — Configuration & Visualization

  • Build the product configuration interface and the geospatial data visualization layer (the common operating picture) the end customer will actually use.

What We’re Looking For

Required

  • Architecture contribution, ready to lead: you’ve been part of a small team that designed and built a system with real architectural stakes — edge/embedded software, sensor or data pipelines, or similar — and you’re ready to own that kind of system yourself for the first time.

  • Data pipeline and storage experience: comfortable designing and operating time-series data systems and message-based ingestion pipelines (MQTT, Kafka, or equivalent), including handling for intermittent/unreliable connectivity.

  • Full-stack range: comfortable moving between backend/data pipeline work (Python, Go, or similar) and frontend (React/TypeScript) without needing a specialist for each; enough familiarity with embedded/edge constraints to architect around them, even if you’re not writing firmware yourself.

  • At least one sensing/signal domain: hands-on experience with at least one of acoustic, EO/IR, or radar signal processing or detection — and genuine excitement to pick up the others as the product grows into them.

  • Cloud infrastructure ownership: able to stand up and maintain CI/CD and cloud infrastructure (AWS preferred; GovCloud experience a plus) without a dedicated DevOps function yet in place.

  • Step-up temperament: proven ability to build and rebuild quickly with loose specs, and comfort taking ownership of a decision you’d previously have brought to someone else.

Strongly Preferred

  • TAK (or similar government system) integration experience: hands-on work integrating with TAK (ATAK/WinTAK/TAK Server) or a comparable government C2/situational-awareness system is a significant plus.

  • Applied ML experience, particularly anomaly detection or pattern-of-life modeling.

  • Prior experience in defense or dual-use tech, including familiarity with the general shape of DoD software requirements (security, data handling, eventual ATO/RMF processes).

  • Geospatial visualization experience (Mapbox, Cesium, deck.gl, or similar).

  • Prior experience on an autonomous systems, robotics, or maritime/subsea platform.

What Valinor Offers

  • Competitive salary, equity packages, and benefits, including health, dental, and vision insurance – fully covered for employees, 401K, development stipends, among others.

  • Unlimited PTO and two-week company holiday at the end of every calendar year.

  • We are a pro-mental health and pro-family company – we actually encourage employees to spend time with themselves and their families. Valinor also provides fertility benefits to those just beginning that journey.

  • Fun work environment – we like to laugh and take care of each other, but we also deeply respect the mission in front of us.

This role requires the candidate be eligible to obtain and maintain a U.S. security clearance. Active clearance preferred, but not required.

Valinor is an equal-opportunity employer committed to creating a diverse and inclusive workplace. The Valinor team is made up of incredibly talented and unique individuals, who together are disrupting industry norms by creating new paths towards the future of defense and government technology. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law, including those with a criminal history, in a manner consistent with the requirements of applicable state and local laws, including the CA Fair Chance Initiative for Hiring Ordinance. We actively encourage members of recognized minorities, women, Veterans, and those with disabilities to apply, and we work to create a welcoming and supportive environment for all applicants throughout the interview process. If you are someone passionate about working on problems that have a real-world impact, we’d love to hear from you!

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a high-complexity architecture ownership role spanning edge systems, cloud pipelines, multimodal sensing, applied ML, and frontend visualization. The candidate must make consequential technical tradeoffs quickly in a small, loosely specified product environment.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$175,000
US market range (per year)$145k–$215k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary range is disclosed. The yearly compensation estimate is inferred from the U.S. market for a senior-level systems software engineer owning architecture across defense-oriented edge, cloud, data, and geospatial products.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you decide which sensor-processing workloads should run at the edge versus in the cloud?

I would begin with latency, bandwidth, reliability, power, security, and model-complexity constraints. Time-critical filtering, initial detection, compression, and operation during disconnected periods generally belong at the edge, while computationally intensive enrichment, long-term analytics, and model training are better suited to the cloud. I would document the decision boundaries and design a protocol that supports replay, versioning, and graceful degradation.

How would you design a reliable ingestion pipeline for intermittent sensor connectivity?

I would use durable local buffering, idempotent message identifiers, acknowledgements, retry policies with backoff, and ordered replay where required. A message broker such as MQTT can support constrained devices, while backend services can validate, persist, and route events to time-series and object storage. Observability around delivery lag, dropped messages, schema errors, and device health would be essential.

What is your approach to fusing data from different sensing modalities?

I would first establish a common time, location, confidence, and metadata model for every observation. Fusion should account for each sensor's uncertainty, coverage, sampling behavior, and failure modes rather than treating all inputs as equally reliable. I would start with explainable rules or probabilistic correlation methods and introduce ML only where measured performance justifies the added operational complexity.

How would you establish CI/CD and infrastructure practices for a small team building a sensitive product?

I would define infrastructure as code, isolated environments, least-privilege access, automated tests, artifact versioning, and auditable deployment workflows. The initial pipeline should make safe releases easy while including security scanning, dependency controls, secrets management, and rollback capability. I would keep the platform intentionally simple at first and evolve it based on delivery and compliance needs.

Describe how you would make an architectural decision when requirements are incomplete and the product is changing quickly.

I would identify the irreversible decisions, clarify the operational assumptions, and choose the smallest design that preserves important future options. I would record the rationale, alternatives, risks, and validation criteria in a lightweight architecture decision record. Then I would build a thin vertical slice to test the highest-risk assumptions with users and engineering stakeholders before scaling the solution.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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