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Staff Machine Learning Engineer, Object Detection

Who is Flock?

Flock Safety is an all-in-one technology solution to eliminate crime and keep communities safe. Our intelligent platform combines the power of communities at scale - including cities, businesses, schools, and law enforcement agencies - to shape a safer future together. Our full-service, maintenance-free technology solution is trusted by communities across the country to help solve and deter crime in the pursuit of safer communities for everyone.

Our holistic public safety platform is comprehensive and intelligent, providing the actionable evidence needed to solve, deter and reduce crime across neighborhoods, schools, businesses and entire cities. Without compromising transparency or privacy, we are turning unbiased data into objective answers.

Flock strives to offer a career-defining experience where you can also make an impact on your community. While safety is a serious business, we are a supportive team that is optimizing the remote experience to create strong and fulfilling relationships even when we are physically apart. Our group of hard-working employees thrive in a positive and inclusive environment, where a bias towards action is rewarded. 

We have raised over $700M in venture capital from investors including Tiger Global, Andreessen Horowitz, Matrix Partners, Bedrock Capital, Meritech Capital Partners, and Initialized Capital. Now surpassing a $7.5B valuation, Flock is scaling intentionally and seeking the best and brightest to help us meet our goal of reducing crime in the United States by 25% in the next three years.

The Opportunity

Flock’s LPR and Video devices detect and track objects to provide its customers with the best information to make actionable decisions. The detection models that are used in these devices need to handle a myriad of different environments, lens configurations, fields of view, and purposes, all within power constraints and hardware limitations. As Staff ML Engineer, Object Detection, you will own the design, training, evaluation, and management of these sets of models to achieve optimal performance.


The Skillset

  • 5+ years of industry experience in Machine Learning with a focus on Object Detection, or related Computer Vision Deep Learning modeling

  • Strong proficiency in PyTorch and/or Tensorflow for training deep learning models

  • Deep understanding of Deep Learning model concepts, such as convolutional networks, transformers, attentions, losses, learning rates, etc.

  • Good understanding of traditional ML (supervised and unsupervised) algorithms

  • Experience tuning and optimizing models for running efficiently on IoT devices

  • Strong software engineering skills in Python

  • Experience with SQL

  • Basic Git knowledge

  • Basic Bash knowledge

  • Nice to have experience in running models on Qualcomm devices

  • Nice to have C++ experience

90 Days at Flock

We are a results-oriented culture and believe job descriptions are a thing of the past. We prescribe to 90 day plans and believe that good days lead to good weeks, which lead to good months. This serves as a preview of the 90 day plan you will receive if you were to be hired as a Software Engineering Manager at Flock Safety. 

The First 30 Days

  • Onboard and Integrate:

    • Familiarize yourself with the company's mission, products, and development processes.

    • Build relationships with key stakeholders to understand their needs and expectations. Meeting with a member of the following teams:

      • Machine Learning

      • Data Engineering

      • Product

  • Assess and Analyze:

    • Review current modeling code and infrastructure

    • Train an object detection model

    • Collaborate with leadership on dataset strategies

The First 60 Days 

  • Propose experiments to enhance object detection

  • Improve model performance evaluations

  • Improve training metrics

90 Days & Beyond 

  • Ability to perform role with little guidance with transparency.

  • Communicating across multiple teams to solve problems efficiently.

  • Be comfortable picking up engineering tasks of larger size and more ambiguity.

  • Finish data acquisition

  • Complete object detection experiments

  • Perform Exploratory Data Analysis

The Interview Process 

We want our interview process to be a true reflection of our culture: transparent and collaborative. Throughout the interview process, your recruiter will guide you through the next steps and ensure you feel prepared every step of the way. 

  1. Our First Chat: During this first conversation, you’ll meet with a recruiter to chat through your background, what you could bring to Flock, what you are looking for in your next role, and who we are. 

  2. The Hiring Manager Interview: You will meet with your potential future boss to really dive into the role, the team, expectations, and what success means at Flock. This is your chance to really nerd out with someone in your field.  

  3. The Panel: Learn more about the team, responsibilities, and workflows. You should be prepared to speak about past projects, how you collaborate and communicate with others, and how you live our values. Depending on the team and role you are interviewing for, you may meet with several teammates as well as cross-functional partners. 

  4. The Executive Review: A chance to meet an executive in your function and view Flock from a different lens. Be prepared to ask well-thought-out questions about the company, culture, and more. 


Salary & Equity:

In this role, you’ll receive a starting salary of $205,000 - $240,000 as well as stock options. Base salary is determined by job-related experience, education/training, as well as market indicators. Your recruiter will discuss this in-depth with you during our first chat.

The Perks 

🌴Flexible PTO: We seriously mean it, plus 11 company holidays.

⚕️Fully-paid health benefits plan for employees: including Medical, Dental, and Vision and an HSA match. 

👪Family Leave: All employees receive 12 weeks of 100% paid parental leave. Birthing parents are eligible for an additional 6-8 weeks of physical recovery time.

🍼Fertility & Family Benefits: We have partnered with Maven, a complete digital health benefit for starting and raising a family. Flock will provide a $50,000-lifetime maximum benefit related to eligible adoption, surrogacy, or fertility expenses.

🧠Spring Health: Spring Health offers a variety of mental health benefits, including therapy, coaching, medication management, and digital tools, all tailored to each individual's needs.

💖Caregiver Support: We have partnered with Cariloop to provide our employees with caregiver support 

💸Carta Tax Advisor: Employees receive 1:1 sessions with Equity Tax Advisors who can address individual grants, model tax scenarios, and answer general questions. 

💚ERGs: We want all employees to thrive and feel like they belong at Flock. We offer three ERGs today - Women of Flock, Flock Proud, and Melanin Motion. If you are interested in talking to a representative from one of these, please let your recruiter know.

💻WFH Stipend: $150 per month to cover the costs of working from home.

📚Productivity Stipend: $250 per year to use on Audible, Calm, Masterclass, Duolingo, Grammarly and so much more.

🏠Home Office Stipend: A one-time $750 to help you create your dream office.

Flock is an equal opportunity employer. We celebrate diverse backgrounds and thoughts and welcome everyone to apply for employment with us. We are committed to fostering an environment that is inclusive, transparent, and collaborative. Mutual respect is central to how Flock operates, and we believe the best solutions come from diverse perspectives, experiences, and skills. We embrace our differences and know that we are stronger working together.

If you need assistance or an accommodation due to a disability, please email us at careers@flocksafety.com. This information will be treated as confidential and used only to determine an appropriate accommodation for the interview process.

At Flock Safety, we compensate our employees fairly for their work. Base salary is determined by job-related experience, education/training, as well as market indicators. The range above is representative of base salary only and does not include equity, sales bonus plans (when applicable) and benefits. This range may be modified in the future. This job posting may span more than one career level.

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What You Should Know About Staff Machine Learning Engineer, Object Detection, Flock Safety

At Flock Safety, we're on a mission to reduce crime in communities through innovative technology and intelligent solutions. As a Staff Machine Learning Engineer for Object Detection, you will be pivotal in enhancing our LPR and video devices that detect and track various objects, providing critical insights for our clients. In this role, you’ll design, train, and evaluate advanced models that can adapt to different environments, lens configurations, and power constraints. Your expertise in deep learning, especially with tools like PyTorch and TensorFlow, will enable you to develop robust solutions that meet the needs of our mission. You'll collaborate closely with cross-functional teams, ensuring that your models are not only performant but also efficiently deployable on IoT devices. The supportive culture here at Flock encourages innovation and continuous learning, allowing you to make a real impact while working remotely. We value your independence and initiative, and your contributions will help drive our vision of reducing crime by 25% within the next three years. If you're passionate about utilizing machine learning to enhance community safety, then joining our dynamic team at Flock could be your next great opportunity!

Frequently Asked Questions (FAQs) for Staff Machine Learning Engineer, Object Detection Role at Flock Safety
What are the primary responsibilities of a Staff Machine Learning Engineer at Flock Safety?

As a Staff Machine Learning Engineer at Flock Safety, your key responsibilities include the design, training, evaluation, and management of object detection models used in our LPR and video devices. You'll analyze various environments and configurations to enhance model performance while also collaborating with teams across the organization to ensure practical applications of your work.

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What qualifications does Flock Safety seek for the Staff Machine Learning Engineer position?

Flock Safety seeks candidates with over 5 years of industry experience in machine learning, particularly in object detection and computer vision. A strong proficiency in deep learning frameworks like PyTorch and TensorFlow is essential. Candidates should also possess solid software engineering skills in Python and have experience optimizing models for IoT devices.

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How does Flock Safety support the professional growth of its Staff Machine Learning Engineers?

Flock Safety fosters a supportive environment by encouraging collaborations, offering access to resources for continuous learning, and prioritizing a culture that values transparency and collective problem-solving. Throughout your journey, you’ll have the chance to engage with various teams and enhance your skill set, making a significant impact on community safety.

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What does the interview process look like for the Staff Machine Learning Engineer role at Flock Safety?

The interview process at Flock Safety is designed to be a collaborative and transparent experience. It includes an initial chat with a recruiter, an interview with the hiring manager, a panel discussion with team members, and an executive review. This allows candidates to engage deeply with various facets of the company and role.

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What is the work culture like at Flock Safety for a Staff Machine Learning Engineer?

At Flock Safety, work culture emphasizes inclusion, collaboration, and mutual respect. The company understands the importance of individual contributions in achieving collective goals. Employees are supported in their roles and encouraged to take initiative while enjoying a flexible work environment.

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Common Interview Questions for Staff Machine Learning Engineer, Object Detection
Can you explain your experience with deep learning frameworks, particularly PyTorch or TensorFlow?

When asking this question, delve into your hands-on experience with PyTorch or TensorFlow. Discuss specific projects where you constructed models, detailing the challenges you faced and how you overcame them using these frameworks.

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How do you approach optimizing machine learning models for efficient performance on IoT devices?

Provide an example of a project where you successfully optimized models for IoT. Discuss techniques like pruning, quantization, or model distillation that lead to faster inference times, reduced memory utilization, and overall improved performance on constrained hardware.

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What strategies do you use for evaluating model performance in object detection?

You should highlight the importance of metrics such as mAP (mean Average Precision), precision-recall curves, and IoU (Intersection over Union). Explain how you would utilize these metrics to assess the effectiveness of your models in practical scenarios.

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Can you discuss a time when you collaborated with cross-functional teams on a machine learning project?

Share a story where you worked alongside product managers, engineers, and data scientists. Highlight how clear communication and shared goals led to successful project outcomes, emphasizing the significance of teamwork in achieving results.

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How do you stay updated on the latest trends and advancements in machine learning and object detection?

Mention resources like research papers, online courses, and professional networks. Discuss how you integrate this knowledge into current projects to ensure your work leverages cutting-edge technology and techniques.

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What challenges have you faced in developing machine learning models, and how did you address them?

Discuss any specific technical hurdles you've encountered, such as overfitting, data imbalance, or integration issues. Focus on the solutions you implemented and how they led to better model performance and reliability.

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Explain a project where data acquisition and exploration played a crucial role in model success.

Provide a detailed example of when thorough data analysis and exploration led to insights that shaped your model's development. Discuss the importance of high-quality data in training effective models.

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How do you prioritize tasks when managing multiple machine learning projects?

Articulate how you assess project urgency, resource availability, and impact when prioritizing your work. Share tools or methods you use for effective time management and project planning.

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Describe your experience with SQL and how you utilize it in machine learning projects.

Talk about specific instances where you used SQL to retrieve, manipulate, or analyze data sets. Highlight how strong data querying skills facilitate smoother workflows and data integration in machine learning.

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How do you ensure alignment with business goals while developing machine learning solutions?

Illustrate how you maintain communication with stakeholders to understand their needs. Demonstrate how you incorporate feedback into your model development process to ensure that the solutions align with overall business objectives.

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Flock Safety provides the first public safety operating system that empowers private communities and law enforcement to work together to eliminate crime.

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Badge Diversity ChampionBadge Future MakerBadge InnovatorBadge Work&Life Balance
BENEFITS & PERKS
Medical Insurance
Dental Insurance
Vision Insurance
Mental Health Resources
Learning & Development
Equity
Paid Holidays
Paid Time-Off
WFH Reimbursements
Child Care stipend
Maternity Leave
Paternity Leave
DEPARTMENTS
SENIORITY LEVEL REQUIREMENT
TEAM SIZE
EMPLOYMENT TYPE
Full-time, remote
DATE POSTED
March 17, 2025

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