ML Engineering Team Lead - Sovereign AI Engineering
About The Position
At Dream, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; it’s a Dream job. Dream is where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Let’s build something extraordinary together.
Dream's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Built as part of a broader sovereign AI platform, our technology is designed to operate in on-premise, private cloud, and air-gapped environments, enabling nations to maintain full control over their data, infrastructure, and AI capabilities. Central to Dream's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
At Dream, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
The Dream Job
It starts with you - a technical leader driven to build both the ML platform and the engineering team behind it. You care about reliable infrastructure, great developer experience, and growing engineers through real ownership. You'll set the technical direction for Dream's ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - shaping how models reach production across cloud and on-prem, including air-gapped deployments. A significant part of the platform supports large language models, with unique challenges across training, evaluation, and inference in mission-critical environments. You stay close enough to the codebase to debug production issues, unblock your engineers, and make sound architecture calls.
If you want to make a meaningful impact, join Dream's mission and lead the team that builds the ML platform driving Sovereign AI products - this role is for you.
The Dream-Maker Responsibilities
- Set technical direction for the ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - through RFCs, prototypes, design reviews, and build-vs-buy decisions
- Lead and grow a team of ML Engineers - hire, mentor, pair on hard problems, and raise the bar through code and design reviews
- Contribute to critical systems, debug production issues, and maintain deep context on the codebase to inform technical decisions
- Own operational excellence for model serving - set and enforce SLAs, run capacity planning, and keep compute costs predictable
- Establish ML engineering standards - reproducible experiments, automated evals, model packaging, CI/CD for models, and observability
- Support the full lifecycle of Dream's models - from training on domain-specific data to low-latency inference powering production systems
- Work closely with Data Platform, AI, Data Science, and Product teams - translate business priorities into engineering work and manage cross-team dependencies
- Measure and improve developer experience - deploy friction, onboarding time, CI turnaround - as seriously as model performance
The Dream Skill Set
- 6+ years in software engineering, ML engineering, or platform engineering, with hands-on experience building and operating ML infrastructure at scale.
- 2+ years leading an engineering team - hiring, mentoring, conducting design reviews, and shipping alongside your team
- Engineering craft - Strong Python, distributed systems design, testing, secure coding, API design, CI/CD discipline, and production ownership.
- ML platform & serving - Model serving frameworks (e.g., Triton, TorchServe, vLLM, Ray Serve); model packaging, deployment pipelines, and inference optimization
- Training infrastructure - Distributed training pipelines (e.g., frameworks like PyTorch, JAX) experiment orchestration and reproducibility
- ML lifecycle tooling - Feature stores, model registries, experiment tracking (e.g., MLflow, Weights & Biases); dataset versioning and lineage
- Data pipelines - Building training and inference data pipelines; familiarity with tools like Spark, Airflow/Dagster, and streaming ingestion
- Comfortable with AI coding tools like Cursor, Claude Code, or Copilot
Nice to Have:
- Experience operating in constrained environments - on-premise, private cloud, or air-gapped deployments
- Hands-on experience with simulation environments, synthetic data generation, or reinforcement learning workflows
- Platform & infra - Kubernetes, AWS, Terraform or similar IaC, CI/CD, observability, incident response
- Hands-on data science or applied ML experience
Never Stop Dreaming...
If you think this role doesn't fully match your skills but are eager to grow and break glass ceilings, we’d love to hear from you!