Machine Learning Engineer

Mission Context

We are building a causal decision engine for high-stakes systems where reasoning must be explicit, interventions must be provable, and outcomes must be accountable.

What You'll Work On

• Implement and train machine learning and generative models, including LLM-based systems.
• Build and maintain data pipelines for training, evaluation, and inference.
• Support agent-based workflows for reasoning, simulation, and decision support.
• Collaborate with senior engineers and researchers to bring models into production.
• Debug, evaluate, and iterate on models under real performance and reliability constraints.

What We Look For

• Solid foundations in machine learning and modern deep learning.
• Hands-on experience with Python and common ML frameworks (PyTorch preferred).
• Familiarity with LLMs, transformers, or generative models.
• Comfort working with data: cleaning, preprocessing, and evaluation.
• Curiosity, ownership, and a strong desire to learn by building.

Bonus

Exposure to time series data, multimodal models, or causal reasoning concepts is a plus.

Work Style

• Impact-first: focus on building things that work.
• Learning-by-doing: fast feedback, real systems, real users.
• Ownership-minded: take responsibility for what you ship.
• Collaborative: learn from researchers and senior engineers.

Why Join Abel

• Work on real-world AI systems, not toy problems.
• Learn directly from experienced AI researchers and engineers.
• Build foundations in LLMs, agents, and decision intelligence.
• Grow quickly in a high-ownership, low-bureaucracy environment.