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Staff Machine Learning Engineer, Multi-Target Multi-Camera - job 2 of 2

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 products detect and track objects to provide its customers with the best information to make actionable decisions. As a Staff Engineer in MTMC Tracking, you will play a critical role in architecting and deploying large-scale, real-time, and accurate multi-camera tracking solutions, advancing Flock beyond single-camera capabilities. You will work closely with research scientists, ML engineers, infrastructure teams, and operations to develop high-performance systems for tracking people, vehicles, or other objects across multiple cameras.

The Skillset

  • 7+ years of industry experience in Deep Learning and Computer Vision

  • Strong background in multi-target multi-object tracking

  • Experience in metric learning, contrastive learning, and embedding-based ReID models

  • Experience with integrating tracking algorithms like SORT, DeepSORT, ByteTrack, FairMOT, or graph-based tracking into systems

  • Knowledge of probabilistic models (e.g., Kalman Filters, Bayesian filtering) and trajectory prediction.

  • Experience in working with hybrid systems of deployed devices working with cloud processing

  • Strong experience in Python

  • Experience leading projects from R&D to production

  • Experience with SQL

  • Basic Git knowledge

  • Basic Bash knowledge

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

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

  • Build relationships with key stakeholders to understand their needs and expectations.

  • Gain understanding of Flock hardware, ML, and data pipelines.

  • Document and present an overview of device, ML, and Cloud systems.

The First 60 Days 

  • Ability to perform the role with decreased need for guidance: Come up with options of solutions instead of “what should I do?”

  • Design a dev environment and test scenarios.

  • Partner with Program Management.

  • Contribute to active development of MTMC.

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.

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 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, Multi-Target Multi-Camera, Flock Safety

At Flock Safety, we're on a mission to eliminate crime and enhance community safety, and we're looking for a passionate Staff Machine Learning Engineer in Multi-Target Multi-Camera to join our innovative team. In this role, you'll play a key part in our advanced LPR and Video products, which detect and track objects to empower communities with actionable insights. You'll work in collaboration with research scientists and ML engineers to create robust, real-time multi-camera tracking solutions that go beyond traditional methods. Your expertise in deep learning, computer vision, and multi-object tracking will be invaluable as you tackle exciting challenges and lead projects from research to production. We pride ourselves on fostering a positive and inclusive work environment that thrives on innovation and collaboration. With our significant backing and a clear goal of reducing crime in the U.S. by 25% in the next three years, you'll be at the forefront of creating technology that truly makes a difference. As part of our team, you'll not only contribute to building a safer future, but you'll also enjoy a flexible, remote work culture, comprehensive health benefits, and numerous perks aimed at ensuring your well-being and work-life balance. If you're ready to make a meaningful impact in the world through cutting-edge technology, Flock Safety is the place for you!

Frequently Asked Questions (FAQs) for Staff Machine Learning Engineer, Multi-Target Multi-Camera Role at Flock Safety
What are the responsibilities of a Staff Machine Learning Engineer at Flock Safety?

As a Staff Machine Learning Engineer at Flock Safety, your responsibilities include architecting and deploying large-scale, real-time, multi-camera tracking solutions. You will collaborate closely with research scientists, ML engineers, and infrastructure teams to optimize tracking systems for people, vehicles, and more. Your role involves leading projects from the R&D phase to production, ensuring high performance and accuracy.

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What qualifications are necessary for the Staff Machine Learning Engineer position at Flock Safety?

To qualify as a Staff Machine Learning Engineer at Flock Safety, you should have over 7 years of experience in Deep Learning and Computer Vision, with a strong background in multi-target multi-object tracking. Familiarity with tracking algorithms such as SORT or DeepSORT, along with knowledge of probabilistic models like Kalman Filters, is essential. Proficiency in Python and experience leading projects from R&D to production are also required.

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What is the company culture like for Staff Machine Learning Engineers at Flock Safety?

Flock Safety prides itself on a transparent, collaborative, and inclusive company culture. As a Staff Machine Learning Engineer, you'll thrive in a supportive environment where innovation is encouraged, and relationships are valued, even when working remotely. The company emphasizes flexibility, so you can achieve a healthy work-life balance while making a meaningful impact.

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What tools and technologies will a Staff Machine Learning Engineer use at Flock Safety?

In your role as a Staff Machine Learning Engineer at Flock Safety, you'll utilize an array of tools and technologies including Python, SQL, and various machine learning frameworks. You will integrate state-of-the-art tracking algorithms into our systems while working with hybrid environments that involve both deployed devices and cloud processing for enhanced functionality.

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How does Flock Safety support professional growth for Staff Machine Learning Engineers?

Flock Safety is committed to the professional development of its employees, including Staff Machine Learning Engineers. The company offers a variety of stipends for educational resources, supports participation in relevant workshops and conferences, and fosters an environment where knowledge exchange is encouraged, allowing you to grow your skills and advance your career.

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Common Interview Questions for Staff Machine Learning Engineer, Multi-Target Multi-Camera
How do you approach multi-target tracking in a multi-camera system?

In multi-target tracking for a multi-camera system, I focus on robust feature extraction techniques and efficient matching algorithms. I leverage deep learning models for object detection and use graph-based methods to maintain object identities across cameras, ensuring accuracy in tracking.

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Can you explain your experience with metric learning and its relevance to tracking?

I've applied metric learning to improve the accuracy of object matching by creating better embeddings that capture object similarities. This approach enhances our tracking systems by allowing them to differentiate between similar-looking targets effectively.

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Describe a project where you led initiatives from R&D to production.

In a previous role, I led a project involving the development of a real-time tracking system. I managed the research phase, where we explored various algorithms, followed by prototyping and ultimately deploying the system into a live environment, ensuring iterative feedback from users.

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What methods do you use for trajectory prediction?

For trajectory prediction, I employ probabilistic models such as Kalman Filters and Bayesian methods, allowing me to estimate future positions based on past motion patterns. This technique significantly enhances the accuracy of our tracking solutions.

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How do you ensure the scalability of machine learning solutions?

To ensure scalability, I design our systems with modular architecture in mind, enabling components to be optimized independently. Additionally, I leverage cloud resources for handling increased workloads, ensuring our solutions remain efficient under varying conditions.

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What is your experience with integrating tracking algorithms like DeepSORT?

I've successfully integrated DeepSORT into our tracking systems, which improved our ability to handle occlusions and re-identify targets. This algorithm's use of appearance and motion information is instrumental in maintaining accuracy over long ranges.

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How do you approach debugging complex machine learning systems?

I systematically isolate components, utilize logging extensively, and analyze outputs to identify issues. I also involve team members for diverse perspectives, which often leads to insights that expedite the debugging process.

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Describe your familiarity with cloud processing and its applications in machine learning.

I have substantial experience working with cloud services to process large datasets for machine learning applications. This allows us to scale effectively and deploy models that require significant computational power without compromising performance.

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How do you stay updated with advancements in machine learning and computer vision?

I regularly read research papers, participate in online courses, and attend industry conferences. Networking with peers and participating in forums also provides insights into emerging trends and technologies in machine learning and computer vision.

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What do you find most challenging about leading machine learning projects, and how do you overcome it?

The most challenging aspect is aligning team efforts with project goals while navigating technical complexities. I overcome this by establishing clear communication channels and ensuring regular feedback loops, enabling the team to stay aligned and motivated throughout the project lifecycle.

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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.

252 jobs
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BADGES
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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
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EMPLOYMENT TYPE
Full-time, remote
DATE POSTED
March 17, 2025

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