Staff Machine Learning Engineer - VC Backed Startups - Signalfire : Job Details

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 Staff AI and Machine Learning engineers who are excited about joining high-growth startups as founding or early team members. 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.

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/ML talent. If you have any questions, please direct inquiries to [email protected].

We're looking for AI engineers who are: Building cutting-edge AI/ML models and deploying them in production Excited about joining early-stage startups in transformative industries Interested in staying connected with SignalFire's portfolio for future opportunities

Typical roles and responsibilities may include:

• Developing and deploying machine learning (ML) and deep learning models

• Building scalable data pipelines for training and inference

• Working directly with founders to align AI strategies with product roadmaps

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

• Optimizing RAG pipelines, agent architectures, and LLM-powered systems

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

• 6+ years of experience in AI/ML, 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, Kubernetes, Docker, MLflow, Kubeflow, FastAPI, SQL, NoSQL, Airflow, Spark, Kafka, Hadoop, AWS (SageMaker, Lambda, S3), GCP (Vertex AI, BigQuery), Azure (ML Studio, Synapse).