Machine Learning Engineer

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

Job summary

Harrison Clarke is seeking an AI Engineer to turn breakthrough ML research into real-world products. You will work at the intersection of LLMs, ML, data intelligence, and software engineering to build intelligent systems that understand, reason over, and extract value from complex datasets.

You’ll design and deploy scalable machine learning systems, develop NLP/LLM-based solutions, and experiment with prompt engineering and retrieval strategies to improve performance.

Qualifications

• Experience building ML applications in production environments.
• Strong foundation in machine learning, statistics, data science, or applied AI.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, etc.).
• Hands-on experience with large language models, generative AI, or NLP systems.
• Understanding of retrieval, prompt engineering, embeddings, fine-tuning, evaluation, and model performance optimization.
• Experience working with structured and unstructured datasets to generate insights.
• Strong problem-solving abilities and clear communication across teams.

Responsibilities

• Design, build, and deploy scalable ML systems powering intelligent products.
• Develop and optimize NLP/LLM-based solutions for business challenges.
• Experiment with prompt engineering, retrieval strategies, fine-tuning, and model optimization.
• Analyze large datasets to uncover insights for product decisions and model improvements.
• Collaborate with product, engineering, and business stakeholders to translate requirements into AI solutions.
• Evaluate and integrate emerging AI technologies into production systems.
• Build data pipelines, experimentation frameworks, and evaluation systems for continuous improvement.
• Contribute to architecture and evolution of AI capabilities across the organization.

Skills

Python

PyTorch

NLP

LLMs

Data pipelines

Communication

Education

Bachelor's in CS

Tools

Hugging Face

MLflow

Kubernetes

Job description

We're looking for an AI Engineer who is excited about turning breakthrough machine learning research into real-world products that create measurable business impact.

You’ll work at the intersection of large language models, machine learning, data intelligence, and software engineering, helping build intelligent systems capable of understanding, reasoning over, and extracting value from complex datasets. This role offers the opportunity to tackle challenging problems, experiment with emerging AI technologies, and contribute directly to products used by customers at scale.

If you’re passionate about generative AI, language models, and building systems that bridge research and production, we’d love to speak with you.

What You’ll Do

• Design, build, and deploy scalable machine learning systems that power intelligent products and workflows.
• Develop and optimize NLP and LLM-based solutions that solve complex business and operational challenges.
• Experiment with prompt engineering, retrieval strategies, fine-tuning techniques, and model optimization approaches to improve performance and reliability.
• Analyze large and complex datasets to uncover insights that drive product decisions and model improvements.
• Collaborate closely with product, engineering, and business stakeholders to transform ambiguous requirements into impactful AI solutions.
• Evaluate and integrate emerging AI technologies, frameworks, and research developments into production systems.
• Build robust data pipelines, experimentation frameworks, and evaluation systems that support continuous model improvement.
• Contribute to the architecture and evolution of next-generation AI capabilities across the organization.

What We’re Looking For

• Experience building machine learning or data-driven applications in production environments.
• Strong foundation in machine learning, statistics, data science, or applied AI.
• Proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, or similar technologies.
• Hands‑on experience working with large language models, generative AI applications, or NLP systems.
• Understanding of concepts such as retrieval, prompt engineering, embeddings, fine‑tuning, evaluation, and model performance optimization.
• Experience working with structured and unstructured datasets to generate meaningful insights.
• Strong problem‑solving abilities and a passion for tackling complex, open‑ended challenges.
• Excellent communication skills and the ability to collaborate across technical and non‑technical teams.

Bonus Points

• Advanced degree (Master’s or PhD) in Computer Science, Machine Learning, AI, Mathematics, Statistics, or a related field.
• Experience deploying AI and machine learning systems at scale.
• Familiarity with cloud‑native machine learning infrastructure and modern MLOps practices.
• Experience with distributed computing, large‑scale data processing, or analytics platforms.
• Publications, open‑source contributions, or demonstrated involvement in the AI research community.
• Experience building customer‑facing AI products or agentic AI systems.

Why This Role

• Work on frontier AI technologies that are shaping the next generation of intelligent software.
• Solve highly ambiguous, high‑impact problems with significant ownership and autonomy.
• Collaborate with a talented team of engineers, researchers, and builders who move quickly and care deeply about product outcomes.
• See your work deployed into real‑world environments where it delivers measurable value.
• Gain exposure across machine learning, LLMs, data systems, product strategy, and cutting‑edge AI innovation.
• Help define what practical, production‑ready AI looks like over the coming decade.