Principal AI/ML Engineer - VC Backed Startups

Join Signalfire’S Talent Network For Principal Ai/Ml Engineer Roles At Vc-Backed Startups

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 Principal AI/ML Engineers who are excited about driving AI strategy, advancing machine learning research, and scaling AI-powered systems at high-growth startups. 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 talentnetwork@signalfire.com.

Who Should Join?

• We’re looking for AI/ML experts who are:
• ✔ Passionate about developing and deploying cutting-edge machine learning and deep learning models
• ✔ Experienced in architecting scalable AI systems and leading technical teams
• ✔ Excited to push the boundaries of AI research and apply it to real-world business challenges

Typical Roles & Responsibilities

• Architect, develop, and optimize machine learning and deep learning models for production systems
• Research and apply state-of-the-art AI methodologies, including LLMs, transformers, and reinforcement learning
• Lead AI strategy, identifying opportunities for innovation and model optimization
• Develop scalable training and inference pipelines for AI-powered applications
• Work closely with engineering, data, and product teams to integrate AI/ML into business solutions
• Optimize ML models for efficiency, accuracy, and scalability in real-world deployments
• Ensure robust MLOps practices, including model monitoring, retraining, and deployment automation
• Collaborate on AI/ML research publications, patents, and open-source contributions
• Architect, develop, and optimize machine learning and deep learning models for production systems
• Research and apply state-of-the-art AI methodologies, including LLMs, transformers, and reinforcement learning
• Lead AI strategy, identifying opportunities for innovation and model optimization
• Develop scalable training and inference pipelines for AI-powered applications
• Work closely with engineering, data, and product teams to integrate AI/ML into business solutions
• Optimize ML models for efficiency, accuracy, and scalability in real-world deployments
• Ensure robust MLOps practices, including model monitoring, retraining, and deployment automation
• Collaborate on AI/ML research publications, patents, and open-source contributions

Common Qualifications

While each startup has its own hiring criteria, many Principal AI/ML Engineer roles in our network look for:

• 8+ years of experience in AI/ML, deep learning, or applied AI
• Expertise in Python and ML frameworks (TensorFlow, PyTorch, JAX, Hugging Face Transformers)
• Strong background in computer vision, NLP, generative AI, or reinforcement learning
• Experience developing scalable AI pipelines, data processing workflows, and distributed training systems
• Familiarity with big data tools (Apache Spark, Kafka, Hadoop) and MLOps platforms (MLflow, TFX, SageMaker)
• Deep understanding of LLMs, transformer architectures, and retrieval-augmented generation (RAG) pipelines
• Experience with model quantization, fine-tuning, and optimization for performance
• Strong knowledge of cloud environments (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes)
• A track record of technical leadership, mentoring, and driving AI innovation
• Languages & Frameworks: Python, TensorFlow, PyTorch, JAX, Hugging Face Transformers
• MLOps & Data Pipelines: MLflow, Kubeflow, TFX, Apache Spark, Airflow, Ray
• Cloud & Deployment: AWS SageMaker, GCP Vertex AI, Azure ML, Kubernetes, Docker
• Big Data & Storage: Apache Kafka, Hadoop, BigQuery, Snowflake, Redis, NoSQL databases
• Model Optimization: ONNX, TensorRT, pruning, quantization, distillation
• Submit your application to join SignalFire’s Talent Ecosystem.
• We review applications on an ongoing basis to identify strong candidates.
• 8+ years of experience in AI/ML, deep learning, or applied AI
• Expertise in Python and ML frameworks (TensorFlow, PyTorch, JAX, Hugging Face Transformers)
• Strong background in computer vision, NLP, generative AI, or reinforcement learning
• Experience developing scalable AI pipelines, data processing workflows, and distributed training systems
• Familiarity with big data tools (Apache Spark, Kafka, Hadoop) and MLOps platforms (MLflow, TFX, SageMaker)
• Deep understanding of LLMs, transformer architectures, and retrieval-augmented generation (RAG) pipelines
• Experience with model quantization, fine-tuning, and optimization for performance
• Strong knowledge of cloud environments (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes)
• A track record of technical leadership, mentoring, and driving AI innovation
• Languages & Frameworks: Python, TensorFlow, PyTorch, JAX, Hugging Face Transformers
• MLOps & Data Pipelines: MLflow, Kubeflow, TFX, Apache Spark, Airflow, Ray
• Cloud & Deployment: AWS SageMaker, GCP Vertex AI, Azure ML, Kubernetes, Docker
• Big Data & Storage: Apache Kafka, Hadoop, BigQuery, Snowflake, Redis, NoSQL databases
• Model Optimization: ONNX, TensorRT, pruning, quantization, distillation
• Submit your application to join SignalFire’s Talent Ecosystem.
• We review applications on an ongoing basis to identify strong candidates.

💡 Technologies You Might Work With:

What Happens Next?

1. If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
2. No match yet? We’ll keep your profile on file for future AI/ML roles in our portfolio.