Member of Technical Staff — Product Engineering

Kindredventures; San Francisco (CA); On-site; USD 115,000 - 165,000; Full time

Job summary

Kindredventures is hiring software engineers to own the path from trained model to customer value, building production systems that deliver predictions in real time across cloud, VPC, on‑prem, and restricted networks. You will create the surfaces customers touch, from APIs to dashboards, and craft demos to turn frontier research into a product.

You should be a strong generalist with experience deploying ML systems in production, comfortable working with customers, and adept at containerization,

Qualifications

• Experience building and shipping production-grade software.
• Experience deploying ML systems in production.
• Ability to design APIs and frontend dashboards.
• Familiarity with cloud providers and IaC tools.

Responsibilities

• Build and operate production systems delivering real-time model predictions.
• Design the product surface: APIs, data delivery, dashboards.
• Handle packaging, security, observability, and upgrades in diverse environments.
• Create demos and prototypes for prospective customers.
• Work directly in customer environments when needed: on-site deployments.
• Develop tooling and playbooks to generalize solutions.

Skills

Generalist software engineering

Backend systems

APIs

Cloud infrastructure

Frontend frameworks

Customer demos

Tools

Docker

Kubernetes

Terraform

Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

We look for software engineers who are excited to tackle unsolved problems. As our models reach the real world, predictions must arrive reliably, on hard deadlines, in whatever environment our customers operate. Your mission is to own the path from trained model to customer value — the production systems that serve predictions, the surfaces customers touch, and the demos that turn frontier research into a product, making every deployment easier than the one before it.

Responsibilities

• Build and operate the production systems that deliver model predictions to customers around hard real‑time deadlines — owning reliability end to end, from cost efficiency to monitoring, alerting, and incident response
• Design and build the full product surface: backend APIs and data delivery, integration patterns, and frontend dashboards and visualizations that make predictions actionable
• Own the packaging, security, observability, and upgrade machinery to deploy our product into customer environments — cloud, VPC, on‑prem, and restricted networks
• Create product demos and prototypes with and for prospective customers, iterating rapidly alongside go‑to‑market
• Work directly in customer environments when needed: integrate with their data and systems, ship solutions on‑site, and translate what you learn into requirements for research and product
• Design the tooling and playbooks that let solutions built for one customer generalize to the next

We value a relentless approach to problem‑solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

What we’re looking for

• Strong generalist software engineering skills across the stack: backend systems, APIs, cloud infrastructure (GCP, AWS, or Azure), and modern frontend frameworks
• Experience deploying and operating ML systems in production, ideally across diverse or customer‑controlled environments
• Familiarity with containerization, orchestration, and infrastructure‑as‑code (e.g. Kubernetes, Docker, Terraform)
• Comfort working directly with customers: scoping ambiguous problems, building demos under time pressure, and representing the company technically
• Background in scalable model serving & deployment architectures and the systems around them
• Owns deliverables end‑to‑end, from requirements through autonomous execution