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SignalFire

Principal AI/ML Engineer - VC Backed Startups

Posted Yesterday
In-Office or Remote
Hiring Remotely in CA
Senior level
In-Office or Remote
Hiring Remotely in CA
Senior level
Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.
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Join SignalFire’s Talent Network for Principal AI/ML Engineer Roles at 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/ML talent. If you have any questions, please direct inquiries to [email protected].

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.

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

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

💡 Technologies You Might Work With:
  • 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

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.

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