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ML Engineer

Remote
Machine Learning Engineer

ML Engineer

Trunk Tools
Remote
Machine Learning Engineer
Trunk Tools

hackajob is partnering with Trunk Tools to fill this position. Create a profile to be automatically considered for this role—and others that match your experience.

 

ML Engineer 

New York City HQ, Austin, Remote - US Time Zone

Employment Type

Full time

Department

Engineering

Compensation

  • $140K – $200K • Offers Equity

At Trunk Tools, we are tackling the massive $13 trillion+ construction industry. We’re an exceptional team of serial entrepreneurs, brought together by our shared mission: automating construction. Our founding team (SpaceX, Stanford, MIT, Carta, etc.) has successfully built and deployed software in construction for 140k+ users, millions of users beyond the construction space, and worked on +$2 billion of built-environment projects. We aren’t another out-of-touch tech startup, most of our team comes from construction. 

 

We spent the last few years building the brain behind construction. Now we are deploying workflows/agents, starting with Q&A document chatbot, to be ingrained in construction teams’ workflows, ultimately to automate construction. Given our immense traction with several Fortune 500 construction companies,  we are doubling our team (currently 45 FTE) in order to deploy several more agents this year. You will have an opportunity to drive the transformation of a multi-trillion-dollar industry full of waste, risks and inefficiencies.

What you will do and achieve:

  • Develop and deploy AI agents to solve real-world problems in the construction industry

  • Design and implement robust systems that incorporate human-in-the-loop and other feedback mechanism to enhance AI agent decision-making and ensure reliable interaction

  • Create and optimize pipelines for model deployment, monitoring, and scaling in production environments

  • Identify and model relationships between entities within and across different document types

  • Enhance retrieval-augmented generation (RAG) systems for context-aware AI agent responses

  • Design and conduct experiments to evaluate and improve model / agent performance and robustness in real-world scenarios

  • Stay updated with the latest advancements in LLMs, reinforcement learning, agentic workflows and multi-modal systems to enhance AI agent performance

Who you are:

  • MS/PhD in Computer Science, Machine Learning, Artificial Intelligence or a related field

  • 5+ years of experience in machine learning, with a focus on building production-ready systems

  • Proficiency in Python and strong experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch

  • Experience in deploying and scaling AI/ML models using cloud services such as AWS or GCP

  • Strong expertise in agent architectures, reinforcement learning, or other systems built on top of LLMs

  • Familiarity with retrieval-augmented generation (RAG) techniques and integration of external knowledge bases or APIs into AI agents

  • Experience with search and retrieval systems, including vector embeddings, graph databases, and elasticsearch

  • Interest in the construction industry

  • Bonus: Specialized expertise with Natural Language Processing, Computer Vision, Speech Processing, or another focus

  • Bonus: Experience with training or fine-tuning proprietary algorithms

What we offer 😎

🎖️ A close-knit and collaborative early-stage startup environment where every voice is heard and every opinion matters.

💰 Competitive salary and stock option equity packages.

🏥 3 Medical Plans to choose from including 100% covered option. Plus Dental and Vision Insurance!

💰 401K

🤓 Learning & Growth stipend.

🥨 Free lunch provided in NYC and Austin office - you’ll never go hungry with us!

🛫 Unlimited PTO; We truly believe in work-life balance and that hard work should be balanced with time for rest and rejuvenation.

🏝 IRL / In-Person retreats throughout the year.

hackajob is partnering with Trunk Tools to fill this position. Create a profile to be automatically considered for this role—and others that match your experience.

 

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