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Machine Learning Engineer, Perception

About Treeswift:

Treeswift is revolutionizing decision-making for critical energy infrastructure in the face of rising threats like severe storms and wildfires.

After cutting our teeth developing AI models for forests in the timber sector, we are growing rapidly in the utility sector. In the U.S. alone, hundreds of thousands of miles of transmission lines and millions of miles of distribution lines need to be monitored and managed to reduce hazards, such as encroaching vegetation, that can cause outages or fires. With the rapid growth of renewable energy this infrastructure footprint is expected to more than double in the coming years creating more assets to defend than ever before.

Treeswift’s mission is to deliver effective scalable technology to manage large-scale distributed infrastructure, starting with managing the risks posed by vegetation surrounding these assets. We do so by innovating across the technical stack. LiDAR and imagery data is collected by our sensor packs mounted on backpacks and vehicles to seamlessly integrate into customer operations. We then leverage AI (i.e. computer vision, etc…) to transform this data into actionable insights delivered to decision-makers in our software platform. 

We are early in our journey and focused on finding ground truth by working closely with our customers and using this understanding to deliver real value to our customers. To tackle this challenge we have brought together mission-driven robotics experts from top academic institutions (Penn, Caltech, etc…) and professionals with deep industry experience (Palantir) in enterprise software development. We hope you’ll join us.  

About the Role:

Treeswift is seeking a highly skilled and motivated Machine Learning Engineer to join our team. You will play a pivotal role in developing and deploying state-of-the-art computer vision solutions to advance our mission. We are looking for an exceptional candidate with a proven track record of training and deploying computer vision models at scale. If you are a passionate and experienced engineer eager to contribute to the future of land monitoring and management we encourage you to apply. 

This is a full-time, hybrid/2-day a week in person role, with a strong preference for candidates based out of our NYC or Philadelphia office. 

Key Responsibilities 

  • Develop machine learning models that revolutionize our customers’ businesses. Treeswift develops machine learning algorithms that upend the cost and accuracy of vegetation management for energy infrastructure. In this role you will be responsible for bringing innovative ideas and rapid execution to new and existing models. In the course of development, you will collaborate closely with other teams (product, operations etc…) and have an opportunity to interact with end-users. Our machine learning model development focuses on two primary areas: (a) LiDAR point cloud models to classify and segment landscapes and infrastructure and (b) image models to derive vegetation attributes such as species and health. 

  • Create a best-in-class feedback loop to accelerate model development. You will improve Treeswift’s ability to assess model performance and adapt to new operating conditions at scale. Treeswift’s cutting edge model development involves significant investment in a proprietary dataset to train our models.

  • Help Treeswift scale. In this role you will be expected to bring prior experience with commercial machine learning model development and deployment to help Treeswift cost-effectively scale its technology to serve a growing number of customers and use cases. You will be responsible for enabling effective collaboration on model development within the engineering team, and you will contribute to efforts to ensure reliable and robust performance of models in production.

Required Skills 

  • Proven track record of training and deploying machine learning models at scale for commercial use cases. 

  • Experience in semantic segmentation and object detection using point cloud data.

  • Experience creating, curating, and cleaning training datasets

  • Strong programming skills in Python 

  • Expertise in deep learning libraries such as PyTorch, TensorFlow, or similar.

  • Excellent problem-solving and analytical abilities.

  • Exceptional communication and collaboration skills.

Preferred Skills

  • Expertise in instance segmentation demonstrated through relevant projects.

  • Experience with one or more of: Sensor Fusion, SLAM, Visual Odometry.

  • Experience with image processing techniques and computer vision fundamentals.

  • Building and using cloud-based training and inference pipelines.

  • 5+ years of professional experience or 3+ years with an advanced degree. 

  • Experience with deep learning training frameworks such as MLflow, Lightning, Weights and Biases, or similar.

What We Value

  • Mission first. We value low ego team members who focus on working towards the best outcomes for the customer and the business, regardless of who gets the credit. 

  • Truth seeking. We don’t always have perfect information to make decisions, but we seek to constantly get closer to the ground truth and aren’t afraid to learn we were wrong in the process.

  • Owners mindset. If you see an opportunity for improvement, run with it. We believe that good ideas can come from anywhere, no matter your role.

Benefits

  • Comprehensive medical, dental and vision insurance 

  • Life insurance package and disability coverage 

  • Stock options

  • Paid leave for new parents 

  • Unlimited PTO

  • 401K

Salary

The estimated salary range for this position is $160,000-$180,000. Total compensation for this position is determined by skills, qualifications, relevant work experience, location, and other factors. This salary estimate excludes the value of any potential bonuses; the value of any benefits offered; and the potential future value of any long-term incentives. This information is provided per the New York City Human Rights Law. Please note that the range provided is applicable only to New York City-based applicants. Base compensation may vary if the work location is outside of New York City.

Treeswift  is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected status in accordance with applicable law.

If you require any accommodations during the recruitment process, whether it be alternate forms of material, accessible meeting rooms, etc., please let us know and we will work with you to meet your needs. 

Average salary estimate

$170000 / YEARLY (est.)
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$160000K
$180000K

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What You Should Know About Machine Learning Engineer, Perception, Treeswift Inc

Are you ready to make a meaningful impact in energy infrastructure as a Machine Learning Engineer at Treeswift? Based in the vibrant city of New York, you'll have the chance to join an innovative team dedicated to revolutionizing decision-making for critical infrastructure management in the face of natural disasters like severe storms and wildfires. As a Machine Learning Engineer specialized in perception, your expertise in developing state-of-the-art computer vision solutions will be vitally important. You'll dive deep into training and deploying machine learning models that transform LiDAR and imagery data into actionable insights for our customers. Your work will not only help reduce hazards associated with vegetation but also aid in managing the growing footprint of renewable energy infrastructure across the U.S. Collaborating with a diverse team of robotics experts and industry professionals, you'll engage closely with product and operations teams, ensuring that our solutions are both effective and customer-centric. The role offers a hybrid work arrangement, blending on-site collaboration in our NYC office with the flexibility of remote work. If you're passionate about land monitoring and management, and you're eager to drive innovation in this field, then we can't wait to hear from you!

Frequently Asked Questions (FAQs) for Machine Learning Engineer, Perception Role at Treeswift Inc
What qualifications do I need to become a Machine Learning Engineer at Treeswift?

To become a Machine Learning Engineer at Treeswift, you should possess a proven track record of training and deploying machine learning models for commercial applications. Experience in semantic segmentation and object detection using point cloud data is crucial, along with strong programming skills in Python. Familiarity with deep learning libraries like PyTorch or TensorFlow is also necessary for success in this role.

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What is the work environment like for a Machine Learning Engineer at Treeswift?

At Treeswift, the work environment for a Machine Learning Engineer is both collaborative and dynamic. You'll be part of a mission-driven team that values low-ego, innovative contributors focused on achieving the best outcomes for customers. The role includes hybrid work flexibility, allowing you to balance in-person collaboration in our NYC office with remote work.

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What are the key responsibilities of a Machine Learning Engineer at Treeswift?

As a Machine Learning Engineer at Treeswift, you will develop and deploy advanced computer vision models that impact energy infrastructure management. You will also create feedback loops to improve model performance and collaborate across teams to align model development with customer needs. Your role is essential for scaling Treeswift's technology and adapting it to new challenges.

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How does Treeswift support professional growth for Machine Learning Engineers?

Treeswift is committed to fostering professional growth for Machine Learning Engineers through opportunities for collaboration, mentoring, and skill development. By working alongside experts from top academic institutions and industry professionals, you will have access to valuable insights that enhance your expertise and career trajectory in machine learning.

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What types of machine learning models will I be working on at Treeswift?

At Treeswift, you will focus on developing machine learning models primarily in two areas: LiDAR point cloud models for landscape classification and segmentation, and image models for analyzing vegetation attributes such as species and health. Your work will significantly contribute to improving the efficiency and accuracy of vegetation management for energy infrastructure.

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What is the estimated salary range for a Machine Learning Engineer at Treeswift?

The estimated salary range for a Machine Learning Engineer at Treeswift in New York is between $160,000 and $180,000. This range reflects relevant skills, qualifications, and experience, alongside other factors. Total compensation will also include benefits like stock options, unlimited PTO, and comprehensive medical insurance.

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Is experience required for the Machine Learning Engineer position at Treeswift?

Yes, Treeswift requires candidates for the Machine Learning Engineer position to have a proven background in training and deploying machine learning models at scale, ideally with at least 5 years of professional experience or 3 years with an advanced degree. This experience is crucial for delivering impactful solutions in energy infrastructure management.

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Common Interview Questions for Machine Learning Engineer, Perception
Can you walk us through a machine learning project you've led?

When answering this question, provide a detailed overview of a specific project, including the problem statement, your methodology, the technologies you used, and the outcomes achieved. Highlight your role in the project, any challenges faced, and how you overcame them, showcasing your critical thinking and adaptability in machine learning development.

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What techniques do you use for data preprocessing in computer vision?

Discuss various techniques you apply for data preprocessing, such as image normalization, augmentation, and handling missing data. Include any experience with creating and curating training datasets specific to LiDAR or imagery, as well as how you ensure dataset quality for reliable model training.

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How do you evaluate the performance of your machine learning models?

Explain the metrics you utilize to assess model performance, such as accuracy, precision, recall, or F1-score. Detail the processes you follow for validation and testing, including cross-validation techniques and how you adjust your models based on performance feedback.

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What is your experience with semantic segmentation and object detection?

Demonstrate your understanding of semantic segmentation and object detection by sharing relevant projects or experiences where you applied these techniques. Highlight specifics of the problems you solved and the impact of your solutions on the overall project objectives.

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How do you stay updated with the latest trends in machine learning?

Describe your strategies for keeping up with advancements in machine learning, such as attending conferences, participating in webinars, following influential researchers, or contributing to open-source projects. Highlight the importance of continuous learning in your development as an engineer.

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Can you explain your experience with deep learning libraries like TensorFlow or PyTorch?

Share your expertise with deep learning libraries by discussing projects where you've used TensorFlow or PyTorch. Explain how you chose the library based on project needs, and any specific features or techniques you've leveraged to optimize model training.

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What approaches do you take for model deployment?

Talk about your experiences deploying machine learning models in production environments. Include the tools and frameworks you've used for creating cloud-based training and inference pipelines, as well as strategies for ensuring model reliability and performance once deployed.

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How do you handle collaboration with cross-functional teams?

Emphasize your communication and collaboration skills by providing examples of how you've worked with product, operations, and other engineering teams. Discuss how collective input enriches the development process and leads to better model outcomes aligned with user needs.

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What challenges have you encountered in your machine learning career and how did you address them?

Be prepared to share a specific challenge you've faced, whether technical or situational, and the steps you took to address it. Highlight your problem-solving capabilities and how navigating those challenges contributed to your growth and understanding of machine learning.

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What motivates you to work in the field of machine learning for energy infrastructure?

Express your passion for the intersection of machine learning and energy infrastructure. Discuss how your personal values align with Treeswift's mission and the role you see yourself playing in contributing to sustainable solutions that have a positive impact on the environment.

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Treeswift is building a new data and monitoring system for the natural world by pushing the frontier in robotic and machine learning technology. Treeswift provides forest inventories, which are powered by a first-of-its-kind robotic and machine le...

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