What are the responsibilities and job description for the Staff AI Engineer - VC Backed Startups position at SignalFire?
A Unique Opportunity to Join SignalFire’s Portfolio of Top-Tier Startups
At SignalFire, we partner with the most promising early-stage startups shaping the future of technology. Our portfolio spans over 200 groundbreaking companies, including well-known names like Grammarly , Ro , and Color Genomics . We typically focus on B2B and enterprise, with investments across health tech , cybersecurity , developer tools and infrastructure , fintech , legal tech , edtech , and vertical SaaS and AI .
We are seeking exceptional Staff AI Engineers to join our portfolio companies as early team members, working directly with visionary founders and SignalFire’s Talent team. You’ll have the chance to shape the future of AI across industries and bring transformative ideas to life. This is a blanket application and a chance to get exposure to a full portfolio of VC-backed startup opportunities from Seed to Series D—our team and startup founders will review your application and reach out if there’s a match.
Please Note : These are full-time opportunities directly with portfolio startups themselves, not through SignalFire.
What You’ll Do :
Design, develop, and deploy machine learning (ML) and deep learning models for cutting-edge applications.
Build pipelines for data preprocessing, feature engineering, and model training.
Deploy and optimize models in production for real-time and batch processing.
Work directly with founders to align AI / ML solutions with product and business strategies.
Conduct research on emerging AI technologies and integrate state-of-the-art methodologies.
Develop and optimize RAG pipelines, agent architectures, and other LLM-powered systems
What You Bring :
7 years of experience in machine learning, deep learning, or applied AI.
Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or JAX.
Strong background in data manipulation with Pandas, NumPy, and Dask.
Experience with big data tools like Apache Spark, Hadoop, or Kafka.
Familiarity with cloud platforms (AWS, GCP, Azure) and containerization tools like Docker and Kubernetes.
Knowledge of MLOps tools like MLflow, TFX, SageMaker, or DataRobot.
Experience in early-stage environments or lean teams is a plus.
Technologies You’ll 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).
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