Machine Learning Engineer

easysoftgroup • San Francisco, CA

16h

About The Position

The role is for a Machine Learning Engineer who can translate cutting-edge AI research into production-grade systems. This position is at the intersection of research and engineering, involving direct work with model internals to build scalable solutions for high-stakes environments. The team focuses on open-source AI, developing highly optimized and interpretable models that surpass existing solutions, especially in regulated sectors like finance. This role is distinct because it involves intervening in how models 'think', building deterministic, auditable, and reliable systems, and directly impacting real-world, high-stakes AI deployments.

Requirements

• Strong understanding of Transformer architectures and PyTorch internals
• Solid foundation in deep learning theory and model behavior
• Hands-on experience training, fine-tuning, or optimizing LLMs beyond surface-level approaches
• Ability to read research papers and implement what actually matters
• Experience working directly with model internals (weights, activations, representations)
• Strong research + engineering mindset
• High ownership - you don’t ignore problems, you fix them
• Curiosity and ability to quickly absorb new concepts from papers
• Deep intrinsic interest in LLMs and their behavior
• Hands-on approach (you build, not just design)
• Comfort working in a fast-moving, high-trust startup environment

Nice To Haves

• Experience with mechanistic interpretability techniques in practice
• Background working with large-scale or open-source LLMs
• Familiarity with regulated environments (finance, compliance-heavy domains)
• Experience building ML systems that require auditability and strict control
• Contributions to research or open-source projects

Responsibilities

• Translate mechanistic interpretability research into production-grade systems
• Work directly with model internals to improve performance, reliability, and control
• Apply techniques like activation patching, control vectors, and feature-level interventions
• Build evaluation and deployment pipelines for enterprise-grade environments
• Design systems that enforce deterministic policies at inference time
• Continuously experiment, validate, and ship improvements into production

Benefits

• Competitive compensation
• meaningful equity
• Direct impact on core AI systems used in production
• High autonomy and trust - strong technical opinions are expected
• Opportunity to push LLMs beyond current limits and define how they behave in the real world

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed