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Staff Machine Learning Engineer, Multi-Target Multi-Camera - job 1 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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Average salary estimate

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$205000K
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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 revolutionize public safety with our cutting-edge technology solutions, and we're looking for a talented Staff Machine Learning Engineer with a focus on Multi-Target Multi-Camera (MTMC) systems to join our team. You’ll play a pivotal role in designing and deploying robust multi-camera tracking solutions that will elevate our capabilities beyond what's possible with single-camera systems. Collaborating with top-notch research scientists and ML engineers, you'll help us transform vast data streams into actionable insights for crime prevention. Your deep expertise in deep learning and computer vision will enable us to track people, vehicles, and objects seamlessly across multiple environments, enhancing our platform's effectiveness in crime reduction. At Flock, we value contribution and innovation, and as part of our supportive and inclusive team, you'll have the chance to make a real impact in your community. This role offers a unique opportunity to innovate and lead projects from research and development to deployment, while also enjoying the flexibility of remote work and a culture that thrives on collaboration and transparency. If you want to engage your technical skills and have a real-world impact, come explore your future as a Staff Machine Learning Engineer at Flock Safety. We're scaling fast, and we want you to be part of shaping a safer future together!

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

As a Staff Machine Learning Engineer at Flock Safety, your primary responsibilities will include architecting and deploying large-scale, real-time multi-camera tracking solutions, as well as collaborating with research scientists and other teams to advance our machine learning capabilities. You'll lead projects from initial R&D phases all the way through to production, ensuring that our tracking algorithms are effectively integrated and delivering the best possible outcomes.

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What qualifications do I need to apply for the Staff Machine Learning Engineer position at Flock Safety?

To be considered for the Staff Machine Learning Engineer position at Flock Safety, you should have a strong background with at least 7 years of experience in Deep Learning and Computer Vision, particularly in multi-target and multi-object tracking. Expertise in metric learning, contrastive learning, and experience with popular tracking algorithms such as DeepSORT or FairMOT will give you a significant advantage. Familiarity with deployed systems, cloud processing, and a strong grasp of Python and SQL are also key requirements.

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What does the 90-day plan look like for a new Staff Machine Learning Engineer at Flock Safety?

The 90-day plan for a new Staff Machine Learning Engineer at Flock Safety focuses on orientation, relationship building, and gradually increasing responsibilities. In the first 30 days, you’ll familiarize yourself with our mission and products while building relationships with key stakeholders. By the 60-day mark, you'll be expected to operate more autonomously, designing development environments and contributing to active projects. By 90 days, you should be comfortable tackling larger tasks and cross-team communication.

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

Flock Safety boasts a culture that values transparency, collaboration, and innovation. As a Staff Machine Learning Engineer, you will find an inclusive and supportive environment that encourages creativity and promotes strong relationships among team members, even in remote work settings. The team thrives on a bias for action, meaning your contributions will be recognized and rewarded as you work toward making communities safer.

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What kind of projects will I work on as a Staff Machine Learning Engineer at Flock Safety?

In the role of Staff Machine Learning Engineer at Flock Safety, you will tackle projects centered around developing sophisticated multi-camera tracking systems. These projects will involve applying your expertise in machine learning to create real-time, accurate tracking solutions, improving the efficiency of our technology in solving and deterring crime, and aiding law enforcement in various applications.

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Common Interview Questions for Staff Machine Learning Engineer, Multi-Target Multi-Camera
Can you describe a multi-target tracking project you have worked on?

Certainly! When answering this question, focus on detailing the specific challenges you faced in multi-target tracking, the methodologies you employed, and the outcomes of your work. Highlight any techniques such as Kalman Filtering you applied, your collaboration with team members, and how the project advanced your skills or contributed to company goals.

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What experience do you have with integrating tracking algorithms into large-scale systems?

In your response, discuss your hands-on experience with tracking algorithms like SORT or DeepSORT. Highlight how you have integrated these algorithms into systems, the challenges faced during implementation, and how you ensured they operated effectively at scale. Provide examples of the systems and technology stacks you worked with to give context to your experience.

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How do you ensure the accuracy of tracking in a multi-camera setup?

Discuss your approach to ensuring accuracy in your multi-camera tracking solutions. This could include using techniques like embedding-based models for ReID (Re-identification), or applying probabilistic methods such as Bayesian filtering. Highlight how you tested and validated tracking accuracy in your previous projects and solutions.

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How do you approach optimizing the performance of machine learning models?

Talk about your strategies for model optimization, which could involve hyperparameter tuning, selecting the right metrics for evaluation, utilizing techniques for dimensionality reduction, and ongoing model training. Discuss how you’ve structured experiments to systematically improve model outcomes and what tools you used to monitor performance over time.

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What is your experience with R&D in the context of machine learning projects?

Describe your past roles in research and development, emphasizing how you’ve driven projects from conceptualization to production. Detail the nature of the R&D work, how you collaborated with teams, and any innovations or new methodologies you introduced that improved on existing frameworks or models.

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Explain the importance of metric learning in your ML projects.

In your answer, clarify the role of metric learning in enhancing the performance of models in applications such as object tracking or classification. Discuss specific projects where you’ve implemented metric learning techniques and how they contributed to improved outcomes.

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What tools and technologies do you prefer when developing ML systems?

Outline the programming languages, libraries, and frameworks you are proficient in, particularly emphasizing Python, SQL, Git, and any ML frameworks you've used such as TensorFlow or PyTorch. Discuss any tools used for model deployment and monitoring, noting how these technologies have helped streamline your development workflow.

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

Mention your methods for staying current, such as attending conferences, participating in webinars, following influential researchers on platforms like GitHub or arXiv, or contributing to community forums. This shows your commitment to continuous learning and your proactive approach to incorporating the latest advancements into your work.

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Describe a time when you faced a technical challenge in a project. How did you overcome it?

Choose a specific example that showcases your problem-solving skills. Explain the context of the challenge, the steps you took to identify solutions, and the eventual outcome. This will illustrate your creativity and tenacity in dealing with technical issues in machine learning projects.

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What role do you think collaboration plays in machine learning projects?

Discuss how collaboration is essential in machine learning, especially in multi-disciplinary environments like Flock Safety. Share experiences where you've successfully worked with cross-functional teams, how you communicated technical concepts to non-technical stakeholders, and the value gained from diverse perspectives.

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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.

246 jobs
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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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