Founding AI Engineer (Senior/Staff) - VC Backed Startups

🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring AI talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.

At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.

We’re looking to connect with exceptional Founding AI Engineers who are excited about joining high-growth startups as core technical leaders. By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.

Who Should Join?

We’re looking for AI engineers who are: 

✔ Passionate about building AI/ML models from the ground up and deploying them in production
✔ Excited about joining early-stage startups and working directly with founders
✔ Interested in shaping AI strategy and leading the development of AI-powered products

Typical Roles & Responsibilities

• Designing, developing, and deploying machine learning (ML) and deep learning models

• Building scalable data pipelines for preprocessing, feature engineering, and model training

• Optimizing and deploying AI models for real-time and batch processing

• Working closely with founders to align AI strategies with product and business goals

• Researching and integrating state-of-the-art AI methodologies

• Developing and optimizing RAG pipelines, agent architectures, and LLM-powered systems

Common Qualifications

While each startup has its own hiring criteria, many founding AI roles in our network look for:

• 3+ years of experience in machine learning, deep learning, or applied AI

• Strong Python skills with frameworks like TensorFlow, PyTorch, or JAX

• Experience with big data tools (Apache Spark, Kafka, Hadoop) and MLOps platforms

• Familiarity with cloud environments (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes)

• Startup experience or an interest in early-stage environments is a plus

💡 Technologies You Might Work With: Python, TensorFlow, PyTorch, JAX, scikit-learn, Kubernetes, Docker, MLflow, TFX, Kubeflow, FastAPI, Flask, SQL, NoSQL, Apache Spark, Kafka, Hadoop, Flink, Airflow, AWS (SageMaker, Lambda, S3), GCP (Vertex AI, BigQuery), Azure (ML Studio, Synapse).

What Happens Next?

1. Submit your application to join SignalFire’s Talent Ecosystem.

2. We review applications on an ongoing basis to identify strong candidates.

3. If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.

4. No match yet? We’ll keep your profile on file for future AI/ML roles in our portfolio.