SignalFire
Staff Machine Learning Engineer - VC Backed Startups
SignalFire - San Francisco, California, United States, 94199
Work at SignalFire
Overview
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Overview
Join SignalFire's Talent Network for AI & Machine Learning Roles
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 talentnetwork@signalfire.com.
Who Should Join?
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 Common Qualifications
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).
What Happens Next? Submit your application
to join SignalFire's Talent Ecosystem. We review applications
on an ongoing basis to identify strong candidates. If there's a match , a SignalFire talent partner or a leader from one of our startups may reach out directly. No match yet?
We'll keep your profile on file for future AI/ML roles in our portfolio.
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 talentnetwork@signalfire.com.
Who Should Join?
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 Common Qualifications
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).
What Happens Next? Submit your application
to join SignalFire's Talent Ecosystem. We review applications
on an ongoing basis to identify strong candidates. If there's a match , a SignalFire talent partner or a leader from one of our startups may reach out directly. No match yet?
We'll keep your profile on file for future AI/ML roles in our portfolio.