Location: San Francisco, CA (Hybrid: 3–5 days/week in-office)
Compensation: $130K–$200K
Hiring: 1 role
Visa sponsorship: Available
A high-growth AI startup transforming back-office automation for complex business workflows. The company leverages multimodal AI and LLMs to extract, normalize, and link data, enabling more accurate and efficient operations in a traditionally manual industry. The team is small, collaborative, and deeply technical, offering engineers early ownership and influence on product and AI strategy.
High-impact work: Build AI models and features that directly shape business outcomes.
Technical breadth: Work on ML modeling, infrastructure scaling, AI agent workflows, and backend systems.
Early-stage influence: Opportunity to define AI strategy, deployment pipelines, and system reliability standards.
Rapid growth: Join a startup scaling in a complex, high-value industry with strong investor backing.
As an AI Engineer , you will:
Design, train, fine-tune, and deploy multimodal LLMs.
Build and scale AI-driven agent workflows.
Contribute to ML infrastructure for high-volume training and inference.
Collaborate cross-functionally with engineering and domain teams to solve practical AI challenges.
Train, evaluate, and deploy machine learning models.
Scale ML infrastructure and improve reliability of training/inference pipelines.
Integrate AI models into production workflows to solve real business problems.
Develop backend systems to support AI features and automation.
Experience:
1–3 years in machine learning, ideally with LLMs or multimodal AI (open to new grads from top CS programs).
Prior startup experience preferred.
End-to-end experience building practical AI applications (not purely research).
Technical Skills:
Strong knowledge of fundamental ML algorithms, especially LLMs.
Experience fine-tuning and deploying LLMs or building agentic systems.
Production-grade coding skills.
Experience with cloud platforms (AWS, GCP, Azure).
Soft Skills & Mindset:
Excellent communicator and collaborative team player.
Comfortable working in a fast-paced, startup environment.
Hybrid work-friendly (regular in-office presence expected).
Experience with document understanding, data extraction, or workflow automation.
Familiarity with multimodal AI pipelines or inference optimization.
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