Machine Learning Engineer

Job Title: Machine Learning Engineer
Classification: Exempt (Salaried)
Reports to: Director of Engineering 

Location: Remote

About the Role:

We’re looking for a Machine Learning Engineer to join our team and help drive the design, deployment, and evaluation of production-grade ML systems. In this role, you’ll work on applied machine learning problems—from experimental design to model deployment—collaborating with engineers, data scientists, and product stakeholders to deliver impactful solutions.

About inKind:

inKind is a mission-driven company. Its mission is to empower restaurants and enrich customer experiences. 

We achieve this by:

• Providing flexible funding solutions to restaurants, helping them thrive and overcome financial challenges.
• Building a loyalty platform that connects customers with their favorite restaurants and rewards them for their support.
• Creating stronger communities by supporting local businesses and fostering connections between restaurants and customers.

We're on a journey to:

• Transforming the restaurant industry: inKind aims to reshape how restaurants access funding, operate, and connect with customers.
• Creating a thriving restaurant ecosystem: They strive to build a sustainable and prosperous environment for restaurants by providing resources and opportunities.
• Empowering restaurateurs: inKind seeks to equip owners with the tools and support they need to achieve their dreams and build fulfilling careers.
• Enhancing customer experiences: Their goal is to provide customers with diverse and rewarding dining experiences while celebrating local businesses.

And we are looking for passionate developers to join us in building and scaling our technology platform.

Responsibilities:

• Model Development: Build and refine machine learning models using Python ML stack—especially scikit-learn, PyTorch, and TensorFlow.
• Prompt Engineering & Evaluation: Design and test prompts for code models (e.g., Claude Code, Codex) with rigorous evaluation of outputs.
• Experimental Design: Lead A/B testing initiatives with sound statistical methodologies to ensure valid experiment conclusions.
  Productionizing ML Solutions: Deploy models as scalable, containerized services (Docker, Kubernetes), following microservices best practices.
• Search & Relevance Engineering: Architect search and ranking solutions balancing multiple signal types—text vectors, numeric scoring, and filter criteria.
• Insight Communication: Translate technical findings into clear insights and actionable recommendations for both technical and business stakeholders.
  

Essential Requirements:

• Strong experience in machine learning engineering or applied ML roles.
• Strong proficiency in Python and core ML frameworks: scikit-learn, PyTorch, or TensorFlow.
• Hands-on experience with prompt engineering for LLMs or code models (Claude, Codex, GPT family)
• Deep understanding of A/B testing methodologies and statistical analysis.
• Experience deploying models in production environments using Docker and Kubernetes.
• Proven experience designing or optimizing search systems, with an understanding of balancing text-based relevance, numeric factors, and filter mechanics.
• Excellent communication skills with the ability to explain complex concepts to non-technical stakeholders.
• Excellent communication and collaboration skills
• Passion for building impactful products and a desire to make a difference

Nice to have:

• Familiarity with retrieval-augmented generation (RAG) systems.
  Exposure to vector databases or semantic search platforms.
• Experience with CI/CD pipelines for ML workflows.

What We Offer:

• Competitive salary and benefits package
• Opportunity to work on meaningful projects that make a real impact
• Collaborative and supportive work environment
• Chance to learn and grow with a talented team
• On-Site, Remote & Hybrid work culture
  $165,000 - $175,000 DOE