What are the responsibilities and job description for the AI//ML Engineer (NLP & Computer Vision) position at Jobs via Dice?
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This company is hiring a full-time ML // AI Engineer to work on-site in San Francisco, building production machine learning systems for large enterprise customers. The work sits at the intersection of applied ML, NLP, LLMs, and real-world systems engineering, using Python to process unstructured signals like OCR output, application metadata, and behavioral telemetry at scale.
This is a true zero-to-one role. You'll design and ship models that turn noisy enterprise data into measurable insight, where accuracy, latency, and cost all matter. They're looking for someone who's comfortable with ambiguity, enjoys hard problems without a playbook, and wants real ownership. In return, you'll get deep technical growth, direct exposure to founders and customers, and the chance to build systems that are actively used by Fortune 500 organizations.
Required Skills & Experience
Tech Breakdown
You will receive the following benefits:
This company is hiring a full-time ML // AI Engineer to work on-site in San Francisco, building production machine learning systems for large enterprise customers. The work sits at the intersection of applied ML, NLP, LLMs, and real-world systems engineering, using Python to process unstructured signals like OCR output, application metadata, and behavioral telemetry at scale.
This is a true zero-to-one role. You'll design and ship models that turn noisy enterprise data into measurable insight, where accuracy, latency, and cost all matter. They're looking for someone who's comfortable with ambiguity, enjoys hard problems without a playbook, and wants real ownership. In return, you'll get deep technical growth, direct exposure to founders and customers, and the chance to build systems that are actively used by Fortune 500 organizations.
Required Skills & Experience
- Strong Python skills and solid software engineering fundamentals
- Hands-on experience building classification and NLP systems
- Expertise in prompt design and optimization for LLMs (few-shot strategies, token efficiency, reasoning scaffolding)
- Experience designing evaluation frameworks, including ground truth creation, A/B testing, and accuracy analysis
- Production ML experience: deployment, monitoring, and performance tuning
- Comfort navigating ambiguity and unexplored technical terrain
- Experience with hybrid ML and rule-based architecture
- Background in OCR, document intelligence, or computer vision
- Proven approaches to cost control in LLM-centric systems
- Experience with PyTorch or similar frameworks
- Familiarity with process mining or workflow analytics
- A track record of shipping ML systems that operate at scale under real constraints
- Personal projects that demonstrate technical depth and curiosity
Tech Breakdown
- 50% NLP
- 50% LLM Orchestration
- 50% Hands On
- 50% Team Collaboration
You will receive the following benefits:
- Medical, Dental, and Vision Insurance
- Vacation Time
- Equity