🛑 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 and machine learning leaders. 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 Heads of AI/ML, including Director- and VP-level leaders, who are excited about defining AI strategy, building high-performing teams, and translating emerging technologies into differentiated products and business outcomes.
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 leaders who are:
✔ Passionate about building AI-native products and applying machine learning to meaningful customer problems
✔ Experienced in defining AI/ML strategy and leading teams from research and experimentation through production deployment
✔ Excited to partner with founders, product leaders, and engineering teams to shape company and product direction
✔ Comfortable balancing technical depth, organizational leadership, and commercial impact
Define and execute the company’s AI and machine learning strategy in alignment with product and business priorities
Build, lead, and develop high-performing teams across machine learning, applied AI, data science, and research
Identify high-impact opportunities to apply AI and translate them into differentiated product capabilities
Lead the development, evaluation, deployment, and continuous improvement of production ML systems
Establish technical standards for model quality, experimentation, reliability, observability, and responsible AI
Guide decisions across model selection, fine-tuning, retrieval, data strategy, infrastructure, and build-versus-buy tradeoffs
Partner with engineering and product leaders to integrate AI capabilities into scalable customer-facing products
Oversee data collection, labeling, governance, and feedback loops required to improve model performance
Evaluate emerging models, research, and tooling while maintaining a practical focus on customer and business value
Communicate AI strategy, capabilities, limitations, and investment priorities to executive teams, boards, customers, and partners
Support recruiting, organizational design, and workforce planning for the company’s AI and ML functions
Help establish safeguards around privacy, security, bias, explainability, and regulatory requirements
While each startup has its own hiring criteria, many Head of AI/ML roles in our network look for:
10+ years of experience across machine learning, artificial intelligence, data science, or software engineering, including meaningful leadership experience
Proven experience building and scaling AI/ML teams in startup or high-growth technology environments
Track record of developing and deploying machine learning systems into production
Strong technical foundation across modern ML methods, model evaluation, data pipelines, and production infrastructure
Experience applying large language models, generative AI, deep learning, or traditional machine learning to real-world products
Ability to connect technical investments to product differentiation, customer outcomes, and business value
Experience partnering closely with product, engineering, data, and go-to-market leaders
Strong judgment around model quality, latency, cost, scalability, safety, and reliability
Ability to operate effectively across hands-on technical leadership, team management, and executive-level strategy
Advanced degree in computer science, machine learning, statistics, mathematics, or a related field may be preferred but is not always required
Languages & Frameworks: Python, PyTorch, TensorFlow, JAX, scikit-learn, Hugging Face
Generative AI: Large language models, multimodal models, retrieval-augmented generation, fine-tuning, prompt engineering, agentic systems
Data & Infrastructure: Spark, Databricks, Snowflake, Kafka, Airflow, vector databases, feature stores
Cloud & MLOps: AWS, GCP, Azure, Kubernetes, Docker, MLflow, Weights & Biases, SageMaker, Vertex AI
Models & Platforms: OpenAI, Anthropic, Google, Meta, open-source foundation models, proprietary model architectures
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 and machine learning leadership roles across our portfolio.
