About 1001

1001 builds AI-powered operational intelligence for the world's most complex, data-heavy environments. We turn fragmented data into a live, unified model of operations and use it to drive better decisions and solve high-stakes problems.

Our work sits inside government and large enterprises, in environments defined by critical operations and messy, real-world data. Our engagements start with forward-deployed teams embedded in the customer environment. They work on real data, build quickly, and iterate until the system proves itself, then scale it across the organization.

The company is backed by Lux Capital, General Catalyst, CIV, Hanabi, Sanabil, and 9Yards, with angels including Chris Ré, Amjad Masad, Karim Atiyeh, Kareem Amin, and Russell Kaplan.

About the role

We are looking for a Machine Learning Engineer to build the models and intelligent systems behind 1001's AI products, from experimentation through production. You will work across machine learning, deep learning, applied AI, data, evaluation, and software engineering.

This role sits between an Applied AI Engineer and an ML Researcher. You should be comfortable with strong ML fundamentals, developing and adapting models, running rigorous experiments, and turning research ideas into reliable production systems.

What you'll work on

• Develop, train, fine-tune, and evaluate machine learning and deep learning models.
• Build solutions using LLMs, multimodal models, agents, and retrieval systems.
• Formulate ambiguous problems as clear ML tasks with measurable evaluation criteria.
• Design experiments, baselines, and evaluation frameworks.
• Analyze model failures and improve performance through data and modeling changes.
• Ship end-to-end ML features from prototypes to production.
• Build reliable inference, data, and evaluation pipelines.
• Apply relevant research and new techniques to practical problems.
• Diagnose issues across models, data, and software systems.
• Work closely with researchers, engineers, and product teams to ship measurable outcomes.

Requirements

• At least 4 years of experience in machine learning, applied research, or software engineering with substantial ML work.
• Strong understanding of machine learning, statistics, and deep learning fundamentals.
• Strong Python skills and experience with modern ML and deep learning frameworks.
• Experience with modern LLMs, multimodal models, agents, retrieval systems, fine-tuning, and evaluation.
• Strong ability to design experiments, evaluate models, and analyze results.
• Ability to take ML problems from formulation and prototyping through production.
• Strong software engineering fundamentals and the ability to write maintainable production code.
• Ability to read and apply relevant ML research.
• Good judgment around model quality, reliability, latency, complexity, and cost.
• Effective use of AI coding tools while remaining accountable for the resulting work.
• Clear communication and the ability to work across broad technical problems.

Nice to have

• Experience operating ML systems in production.
• Experience with model serving, monitoring, containers, cloud infrastructure, or Kubernetes.
• Experience optimizing inference or GPU workloads.
• Experience with forecasting, ranking, recommendation, or other applied ML problems.
• Experience with hybrid, on-prem, or sovereign-cloud deployments.
• Experience with reinforcement learning, optimization, or simulation.
• A background in operations research, applied mathematics, or statistics.

Working at 1001

We take on high-stakes problems in environments where mistakes carry real consequences. That demands an uncompromising bar, real speed, and systems that hold up under live operations. The people who thrive here set that bar for themselves and keep raising it. They own outcomes from end to end, bring rigor to everything they do, and lift everyone around them.