Junior Generative AI Engineer

We believe in building engineers, not just hiring them.

As a junior member of the team, you'll receive:

• Dedicated mentor: A senior engineer assigned as your day-to-day mentor for at least your first six months — available for architecture discussions, code review, and career guidance
• Structured onboarding: A 90-day onboarding plan covering our stack, legal domain fundamentals, and AI evaluation practices so you're never left guessing what to learn next
• Pair programming: Regular pair-programming sessions with senior engineers on production features — the fastest way to absorb patterns, tooling, and judgement
• Code review as learning: Every pull request receives a thorough, constructive review. You'll also review others' code early on, reading good code is one of the best ways to write it
• AI safety and bias training: Guided training on identifying non-deterministic failure modes, prompt injection risks, bias in LLM outputs, and the responsible-AI practices that govern our systems
• Growth path: A clear progression framework from Junior to Mid-Level to Senior, with regular check-ins, stretch goals, and the opportunity to take on increasing ownership as your skills develop

• Some software development experience (internships, co-ops, or substantial academic/personal projects count), including hands-on exposure to LLM/GenAI tools or APIs and some experience working with text data (parsing PDFs or HTML, basic text processing, or using an LLM for extraction or classification)
• Solid Python skills and comfort with basic SQL. Conceptual understanding of how RAG pipelines work, with ideally some hands-on experience. Familiarity with at least one LLM API (OpenAI, Anthropic, or similar). Exposure to an orchestration framework (LangChain, LlamaIndex) or a vector database is a plus
• Basic familiarity with cloud platforms (AWS or Azure) and an understanding of how to evaluate generative AI outputs (accuracy, faithfulness, hallucination). You don't need deep expertise in either area, but you should understand the fundamentals and be willing to learn
• Strong communication skills: ability to explain what you've built, ask good questions, and clearly describe problems and trade-offs to both technical and non-technical teammates

Nice to Have:

• Experience in a hackathon, open-source project, or fast-paced team environment where you shipped working software quickly
• A degree (Bachelor's or Master's) in Computer Science, Mathematics, Computational Linguistics, or a related quantitative field
• Hands-on experience with a vector database or retrieval system (Pinecone, Weaviate, pgvector, FAISS)
• Curiosity about model-serving infrastructure (vLLM, TGI) or how open-source models are deployed — you don't need production experience, but interest matters
• Any exposure to legal tech, compliance tech, or other regulated industries where accuracy and auditability are critical
• A personal blog, portfolio project, or open-source contribution demonstrating your interest in NLP, information retrieval, or generative AI
• Interest in or coursework on knowledge graphs, ontologies, or semantic reasoning
• Any experience with Spark, Databricks, or data pipeline tooling

The estimated base salary range for this position is $120,000 to $160,000 at the time of posting.

The actual salary offered will depend on a variety of job-related factors, including skills, education, training, credentials, experience, scope and complexity of role responsibilities, geographic location, and performance.

This role is exempt meaning it is not overtime pay eligible.

Cleary provides a comprehensive benefits package, including health care benefits.

More information can be found here: Benefits

We are an equal opportunity employer and prohibit discrimination based on any category protected by law.

Cleary provides reasonable accommodations to enable otherwise qualified employees to perform the essential functions of their position, provided the accommodation does not pose an undue hardship to the Firm.

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