Machine Learning Software Engineer

Harrison Clarke; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Harrison Clarke is seeking a Software Engineer with Machine Learning expertise to join a mission-driven team focused on safer, traceable AI. The role involves analyzing model behavior, identifying failure modes, and building real-time systems to improve AI safety.

You’ll work on end-to-end ML solutions with a strong emphasis on clarity and accountability. The ideal candidate brings hands-on NLP experience with LLMs and transformers, plus a track record of moving from prototype to production in

Qualifications

• Hands-on experience working with modern NLP systems in real-world contexts (LLMs, embeddings, transformers, etc.).
• Comfort moving from prototype to production in Python—outside the notebook.
• Experience building or working with evaluation pipelines and frameworks.
• Practical thinking, sharp debugging skills, and an appetite for ambiguity.

Responsibilities

• Hands-on experience with NLP systems in real-world contexts (LLMs, embeddings, transformers).
• Python production readiness beyond notebooks.
• Build or maintain evaluation pipelines and frameworks.
• Drive practical thinking, debugging, and handling ambiguity.

Skills

Machine Learning

NLP

Debugging

Analytical thinking

Communication

Tools

Python

Evaluation pipelines

Transformers

Job description

The Role

We’re seeking a Software Engineer with Machine Learning expertise to join our mission-driven team, diving beyond surface-level metrics to analyze model behavior, uncover subtle failure modes, and develop real-time systems that enhance AI safety. You’ll focus on building safer, traceable, and accountable AI solution during the most critical phase of AI’s evolution.

What You’ll Own

• Hands-on experience working with modern NLP systems in real-world contexts (LLMs, embeddings, transformers, etc.).
• Comfort moving from prototype to production in Python—outside the notebook.
• Experience building or working with evaluation pipelines and frameworks.
• Practical thinking, sharp debugging skills, and an appetite for ambiguity.

Ideal Signals

• Experience using or building tools that evaluate the behavior of language models (LLMs).
• Background in environments where trust, safety, or compliance is critical—even if outside traditional “regulated” industries.
• Hands-on experience testing AI systems for edge cases, failure modes, or unexpected behavior.

Why This Role?

Join an early-stage, well-funded, mission-driven startup where you’ll enjoy technical autonomy and direct exposure to customer use cases, allowing your code to significantly shape the company’s trajectory. You’ll thrive in a culture that prioritizes clarity, urgency, and respect, valuing impactful contributions over self-promotion. Collaborate with a sharp, kind, high-trust team dedicated to delivering innovative solutions in a high-stakes environment.