Build and operate reliable, scalable production systems for Timescapes. Responsibilities include improving availability, observability, incident response, cloud infrastructure, deployment automation, and operational readiness. The role also architects ML platforms supporting data preparation, training, deployment, monitoring, and iteration, while applying AI to engineering operations. The engineer partners across Software, AI, Product, and Hardware teams to balance reliability, security, scalability, developer experience, and cost.
Timescapes is looking for a Senior Site Reliability Engineer to join our small, but growing team of high-performers. We’re after someone who approaches problems from first principles, identifying underlying causes and diligently iterating towards the best solution. We believe that great products feature incredible experiences that customers never want to stop using.
Hybrid - Austin, TX: 2 to 3 days per week, but very flexible
About us
Founded in New Zealand in 2017, Timescapes is a visual progress tracking solution for complex construction projects. Our mission is to simplify construction through shared visibility. Customers love Timescapes because it helps them stay on schedule, validate construction claims and communicate progress more effectively.
We’re a rapidly growing company, and are used by some of the largest construction firms across Australia, Canada, New Zealand and the United States. We’re deliberate and thoughtful in everything that we do, and we emphasize awareness, care and quality in our approach to product development.
About the role
As our Site Reliability Engineer, you’ll be responsible for building and operating the reliable, scalable systems that underpin Timescapes’ product and engineering teams. This is a broad, hands-on role spanning traditional site reliability engineering, ML platform architecture and the applied use of AI to improve engineering operations. Key aspects of the role include:
• Reliability and production operations: owning the availability, performance and resilience of our production systems, with a pragmatic approach to capacity, risk, cost and operational readiness.
• Observability and service health: defining meaningful SLIs and SLOs, strengthening instrumentation and alerting, and making system behaviour easier to understand before customers are affected.
• Incident management and learning: participating in and improving on-call practices, leading effective incident response and turning post-incident learning into durable improvements rather than temporary fixes.
• Platform automation and delivery: improving cloud infrastructure, deployment pipelines and environment management so engineers can ship safely with less operational toil.
• ML platform architecture: driving the architecture that supports data preparation, model training, evaluation, deployment, monitoring and iteration, in close partnership with our AI / ML engineers.
• Applied AI for engineering operations: identifying and implementing AI-enabled tools and workflows that accelerate diagnosis, improve operational decision-making and remove repetitive work across engineering.
• Cross-functional partnership: working closely with the Software, AI, Product and Hardware teams to make sound trade-offs across reliability, security, developer experience, scalability and cost.
About you
We want curious, driven and enthusiastic people who are motivated by the prospect of building solutions that endure. The people who thrive at Timescapes are critical thinkers who are not afraid to embrace the unconventional. If the following resonates with you, we should definitely chat:
• Production and ML systems experience: you’ve built and operated customer-facing systems and helped design or run production ML infrastructure. You’re comfortable diagnosing complex failures and supporting production software.
• Strong engineering fundamentals: you can write maintainable software, automate infrastructure and reason across distributed systems, networking, cloud services and data-intensive workloads.
• Value-oriented: you’re focussed on ensuring that Timescapes gets genuine value from engineering investments. You use methodologies, best practices and patterns as tools, but never let them get in the way of great customer outcomes.
• Obsessed with the “why”: you care about the details and take pride in sharing the rationale for your decisions. You question things that don’t make sense, and you expect others to do the same.
• All about the editing: you don’t expect to nail the solution on the first attempt. You’re comfortable discarding ideas, learning from production and treating the first release as the beginning.
• Adaptable and creative: you know that what worked in the past may not work in the future. You’re not deterred by ambiguity, and you’re excited about trying new things and finding your way through.
• Communication matters: you communicate clearly and succinctly, build strong working relationships and are equally comfortable on Slack / email, video calls and in person.
The following are definitely not required, but would be a bonus in this role:
• Computer vision infrastructure: you’ve supported media-heavy data pipelines, GPU workloads or computer vision and multimodal models across edge and cloud environments.
• Applied AI tooling: you’ve used LLMs or other AI systems to improve developer workflows, observability, incident response or engineering operations.
• Startup / scale-up experience: you’ve juggled competing priorities, rebuilt unscalable processes and know when to add structure without adding unnecessary bureaucracy.
CompensationThe base pay range for this role is $130,000 – $160,000 per year.
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