Deployment Engineer

present of AI; San Francisco (CA); In loco; USD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

Sieve is seeking a hands-on software engineer in San Francisco to set up and run hardware and data collection sites. The role requires debugging unfamiliar systems, spanning software, hardware, networking, and data uploads, with travel to global sites.

You will train local engineers and build trust with site partners while contributing to AI data infrastructure. You'll work on data pipelines feeding multimodal AI models, learning how real-world data is captured and curated for training.

Competenze

• Hands-on software engineer with hardware/data collection site setup experience.
• Ability to debug unfamiliar systems across software, hardware, networking and data uploads.
• Ability to train local engineers and build trust with site partners.

Mansioni

• Set up and run hardware and data collection sites globally.
• Debug and maintain systems spanning software, hardware and networking.
• Train local engineers and partner with site teams.
• Contribute to AI data infrastructure and deployment tools.

Conoscenze

Software debugging

Travel readiness

Cross-functional collaboration

Hands-on problem solving

Strumenti

Swift

Linux

Embedded systems

Robotics

Descrizione del lavoro

Customer & Solutions · San Francisco · FullTime · Mid level

This role asks for a hands-on software engineer who can travel globally to set up and run hardware and data collection sites. A strong candidate can debug unfamiliar systems, work across software, hardware, networking, and data uploads, and learn quickly on the ground. They also need strong people skills to train local engineers and build trust with site partners.

How it fits the AI era

AI changes the work by making data quality and reliable capture even more critical, since foundation models depend on curated real world data. The role stays human in the field: setting up camera rigs, solving on site problems, and training local teams. Turning field lessons into better deployment tools is where software engineering meets AI infrastructure.

How it builds AI experience

Someone in this role would gain direct experience with the data pipeline that feeds multimodal AI models, from capture to upload. They would learn how real world data is collected and curated for training, which is core to Sieve's work. Building and debugging these systems offers practical insight into AI data infrastructure.

Worth showing

• Software debugging and shipping
• Hardware and networking setup
• Fast problem solving
• Training local engineers

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Sieve

Multimodal artificial intelligence data laboratory creating curated video, audio, and physical world training environments for frontier foundation models, per the company.

Tools mentioned: Swift, Linux, embedded systems, robotics