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Founding Lead AI Engineer - ResiQuant

About Us:

ResiQuant is revolutionizing how the world understands the built world exposed to natural disasters. From earthquakes to wildfires and hurricanes, the impact of climate change and urbanization is intensifying—and yet, decision-makers are forced to rely on fragmented, static, and often inaccurate property data.

ResiQuant is changing that. Our platform automates submission processing and enriches data, enabling insurers to elevate underwriting precision and productivity. Trusted by leading earthquake insurers in the United States, ResiQuant serves as the definitive source of property intelligence. Our AI, grounded in structural engineering expertise, ensures that portfolios dynamically reflect evolving building codes, ordinances, and local construction practices, providing actionable insights for informed decision-making.

We are fueled by an unrelenting focus on protecting lives, property, and livelihoods; ResiQuant is building the future of property risk intelligence at a time when it has never been more crucial.

Join us in redefining disaster resilience and shaping a safer world.

About You:

As the Lead AI Engineer in Computer Vision and Vision-Language Models, you work closely with the founders to design, develop, and deploy advanced AI models that harness the power of computer vision and vision-language capabilities to analyze building data through the lenses of a domain expert. You will lead and mentor a team of engineers, collaborating closely to drive innovations in AI, optimize model performance, and deliver robust, scalable solutions. This role requires a blend of technical expertise, hands-on leadership, and a strong desire to work hard and thrive in a fast-paced, dynamic environment.

Start date: Immediate

Responsibilities:

Technical Leadership: Drive the development and deployment of cutting-edge computer vision and vision-language models, ensuring alignment with company goals and industry best practices.

Team Management: Lead a team of engineers, fostering a collaborative and innovative culture, setting clear goals, and ensuring high-quality deliverables.

R&D and Innovation: Stay updated with the latest research in vision and vision-language models, identifying and integrating new techniques and technologies to maintain a competitive edge.

Model Optimization: Ensure models are efficient, scalable, and optimized for performance across various applications and platforms.

Cross-Functional Collaboration: Work closely with the founders, product managers, data scientists, and other members of the team to align model development with business objectives and product requirements.

Qualifications:

Experience:

  • Minimum of 4 years of experience in computer vision and vision-language models.

  • Experience leading a team of engineers, with a track record of delivering successful AI projects that involve fine-tunning, training, and deploying LLMs.

  • Expertise designing and implementing machine learning architectures using frameworks like TensorFlow, PyTorch, or similar.

Education: Master's or Ph.D. in Computer Science, AI, or a related field with a specialization in computer vision, NLP, or machine learning.

Technical Skills:

  • Strong knowledge of computer vision algorithms, vision-language integration, and multimodal AI models.

  • Proficiency in Python and experience with cloud platforms (AWS, GCP, or Azure) for model deployment.

  • Familiarity with image processing libraries, NLP libraries, and LLMs (both, open source and commercial).

What will make you stand out:

  • Leader of a fast-paced AI development team.

  • Ph.D. in CS with focus on computer vision and/or LLMs.

  • Experience with AI applications using satellite and geospatial imagery.

  • Conference and/or journal publications on ML, deep learning, or LLMs.

  • Proficiency/Experience collecting and interpreting data from interviews.

What drives us:

  • Impact: we are driven by a shared mission to address a paramount challenge of our time

  • Resolve: we believe that hard work and resilience yield extraordinary outcomes

  • Urgency: we are motivated to outpace rapid urbanization and escalating disaster impacts

Why join RQ:

  • Opportunity to be involved in an early-stage startup and build the culture you want to see.

  • Chance to pioneer and disrupt one of the world's largest industries.

  • Experience firsthand the tangible impact of what you build.

Day to day:

  • Research and deploy architectures for AI systems trained on structural engineering and disaster risk domain expertise for classification tasks.

  • Participate in product ideation and development.

  • Interface with product managers, software development, and AI team to understand product goals and data needs.

  • Write, test, document, and review code according to RQ’s development standards that you would help to define.

What we offer:

  • Competitive salary commensurate with experience

  • Equity in the company as an early stage member

  • Vibrant tech startup environment

  • Competitive company 401(k) program with company matching

  • Health insurance

  • Working on the challenge of our generation with other passionate people

Average salary estimate

$125000 / YEARLY (est.)
min
max
$100000K
$150000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Founding Lead AI Engineer - ResiQuant, Pear VC

ResiQuant is on the hunt for a Founding Lead AI Engineer to join our passionate team in Austin, TX. If you've got a knack for computer vision and vision-language models, this could be your chance to make a real difference! At ResiQuant, we're transforming how the world comprehends property risks, especially in the face of natural disasters like earthquakes and hurricanes. Our innovative platform automates data processing to help insurers enhance their underwriting precision and productivity. In this pivotal role, you won't just be another engineer; you'll be a key player working alongside our founders to craft cutting-edge AI models that analyze building data through expert lenses. Your leadership will inspire a dedicated team of engineers as you drive AI innovations, optimize model performance, and produce scalable solutions. Imagine collaborating with product managers and data scientists, integrating new techniques, and maintaining a competitive edge, all while staying in tune with the latest industry research! If you're someone who loves challenges, has at least 4 years in computer vision, and a Master's or Ph.D. in a related field, we want you to lead our AI team. Join ResiQuant, where your work will have a profound impact on disaster resilience. Together, we’re shaping a safer world, one innovative solution at a time!

Frequently Asked Questions (FAQs) for Founding Lead AI Engineer - ResiQuant Role at Pear VC
What are the main responsibilities of a Founding Lead AI Engineer at ResiQuant?

As the Founding Lead AI Engineer at ResiQuant, your main responsibilities include driving the development and deployment of innovative computer vision and vision-language models, managing a team of engineers, and ensuring that all AI solutions align with our company goals. You will also engage in research and development to integrate cutting-edge techniques into our models, optimizing their performance, and collaborating with cross-functional teams to meet product requirements.

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What qualifications do I need to apply for the Founding Lead AI Engineer position at ResiQuant?

To apply for the Founding Lead AI Engineer position at ResiQuant, candidates should have a Master’s or Ph.D. in Computer Science, AI, or a related field with a focus on computer vision, NLP, or machine learning. Additionally, candidates should possess at least 4 years of experience in computer vision and vision-language models, specifically in leading successful AI projects and utilizing machine learning frameworks like TensorFlow or PyTorch.

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What technical skills are essential for the Founding Lead AI Engineer role at ResiQuant?

Essential technical skills for the Founding Lead AI Engineer role at ResiQuant include a strong understanding of computer vision algorithms, expertise in vision-language integration, and proficiency in programming languages such as Python. Familiarity with machine learning architectures, cloud platforms (AWS, GCP, or Azure), and image processing/NLP libraries is also crucial for success in this position.

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How does the Founding Lead AI Engineer at ResiQuant contribute to disaster risk management?

The Founding Lead AI Engineer at ResiQuant plays a critical role in disaster risk management by developing advanced AI solutions that analyze building and property data. By leveraging computer vision and vision-language capabilities, you ensure that our models provide accurate and actionable insights, enabling decision-makers to respond effectively to the increasing threats posed by natural disasters, thereby enhancing overall resilience in affected communities.

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What type of company culture does ResiQuant foster for its Founding Lead AI Engineer?

ResiQuant fosters a vibrant tech startup culture that emphasizes collaboration, innovation, and a shared mission to address significant challenges through AI. As a Founding Lead AI Engineer, you will have the autonomy to shape the team culture, work in a fast-paced environment, and contribute to impactful projects. We're all about hard work, resilience, and immediate action, making it an exciting environment for visionary thinkers.

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Common Interview Questions for Founding Lead AI Engineer - ResiQuant
What experience do you have with computer vision and vision-language models?

When asked about your experience with computer vision and vision-language models, start by discussing specific projects where you developed or deployed such technologies. Highlight the unique challenges you faced and how you overcame them, emphasizing any innovative solutions and the impact they had on the overall project outcome.

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How do you prioritize tasks when leading a team of AI engineers?

In responding to how you prioritize tasks while leading a team, share your approach to assessing project needs, evaluating team strengths, and aligning those with business goals. Discuss your strategy for fostering communication and collaboration within the team to ensure everyone is on the same page and working effectively towards a common objective.

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What techniques do you use to optimize AI models for performance?

When discussing optimization techniques, refer to specific methods you employ like hyperparameter tuning, model pruning, or employing transfer learning. Provide examples of how these techniques improved model efficiency or accuracy, making sure to link them back to the goals of the projects you worked on.

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Can you describe a time you integrated new AI techniques into an existing project?

For this question, recount a situation where you successfully integrated new techniques into an ongoing project. Detail the research process, how you identified the potential improvements, and the steps taken to implement those changes. Emphasize the positive outcomes that followed, such as enhanced performance or efficiency.

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How do you stay updated with the latest research in AI?

Staying updated with the latest AI research is crucial in a fast-evolving field. Discuss your approach to continuous learning, including following prominent journals, attending conferences, participating in webinars, and being active in online AI communities. This shows your commitment to growth and excellence.

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How do you handle disagreements within your team?

When addressing team disagreements, explain your conflict resolution strategy. You might discuss how you encourage open dialogue, active listening, and constructive feedback to reach a consensus. Share an example where you successfully navigated a disagreement and turned it into a productive discussion.

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What is your experience with deploying machine learning models in a production environment?

In your response, focus on your practical experiences with deploying ML models. Discuss the platforms used, the deployment process, any challenges faced, and how you ensured scalability and reliability in a production setting. Highlight the successful outcomes—like reduced latency and improved user experiences.

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Could you share how you mentor junior engineers?

When discussing mentoring, describe your approach in fostering a supportive learning environment. Give examples of how you facilitate knowledge sharing, provide guidance on technical skills, and encourage independent problem-solving among junior engineers. Highlight the growth you've witnessed in your mentees as a result.

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How do you assess the success of an AI project?

In assessing the success of AI projects, discuss the key performance indicators (KPIs) you establish upfront. Include metrics like accuracy, processing time, and user feedback. Talk about your approach to iterating on projects based on these assessments to continuously improve outcomes.

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What excites you most about working at ResiQuant?

Share your enthusiasm for ResiQuant's mission to transform disaster risk management through AI. Highlight how the intersection of technology and impact inspires you, and discuss specific aspects of the company culture or projects that you find particularly appealing and aligned with your career passions.

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Pear Accelerator is the best program for pre-seed and seed-stage founders to launch iconic companies from the ground up. We deliberately keep the program "small batch" to maximize the attention each founder gets from our partners. Our companies ...

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DATE POSTED
December 13, 2024

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