ML Engineer – Applied AI

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NineTwoThree Studio

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2012

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

Artificial Intelligence • SaaS • B2B

NineTwoThree Studio is an award-winning software design, engineering, and marketing studio headquartered in Boston, MA. The company specializes in building AI, web, and mobile applications for established brands and funded startups. With a focus on agile methodology, design thinking, and impeccable engineering, NineTwoThree Studio provides custom software development, generative AI, machine learning, and go-to-market strategies. They are known for their innovative solutions that help brands scale and iterate, supported by a team of 70 experts. The studio has successfully built 75 products and 14 startups, earning recognition as the #1 Boston Agency for four years in a row. They work across multiple industries, offering services in design sprints, product strategy, and mobile app development.

📋 Description

• Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines. • Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment. • Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints. • Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy. • Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection). • Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams. • Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base.

🎯 Requirements

• Proven Track Record: 3+ years of experience engineering software with a strong focus on machine learning and natural language processing. • LLM & Generative AI Mastery: In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. Ability to identify when a deterministic solution is preferable to an LLM or agent-based solution. • Production experience: Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement. • Evaluation experience: Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. Ability to reason about tradeoffs between quality, latency, cost, reliability, and engineering complexity. • Python & SQL Proficiency: Exceptional Python coding skills and the ability to query, clean, and structure data efficiently. • Cloud Infrastructure: Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS. • Ownership: Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support. • Ambiguity to Execution: Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks. • The "Product Engineer" Mindset: Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built. • Communication: Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts. • Adaptability: Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks. • Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).

🏖️ Benefits

• Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years • Paid sick leave, 10 national holidays, and 2 company days off • Well-being budget • Maternity/paternity leave • Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager) • Hardware upon business needs • Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor