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Machine Learning Engineer II - Maintenance

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 Safety is a leading public safety company dedicated to eliminating crime through a full-service offering of hybrid hardware and software systems. As a Machine Learning Engineer you will support the machine learning model lifecycle that allows our devices to decode evidence like a detective and define how crime is eliminated. As an ML Engineer on the Maintenance Team, you will play a key role in improving model performance and addressing model drift. The ideal candidate is someone who is passionate about taking cutting edge research and technology to solve new problems, and enjoys working with the entire lifecycle of Machine Learning software.

The Skillset:

  • BS/MS in Computer Science, Mathematics, Physics, Engineering, or proof of equivalent software engineering experience (PhD’s welcome)

  • Experience solving problems using Machine Learning frameworks (Tensorflow, PyTorch, scikit-learn, etc.)

  • Good understanding of Deep Learning and Traditional ML (supervised and unsupervised) algorithms

  • Experience writing Python in a team environment

  • Able to take on complex problems, learn quickly, iterate, and persist towards a good solution

  • Effectively communicate, at the level of your audience, and seek to understand and be understood

  • Basic SQL knowledge

  • Basic Git knowledge

  • Experience with linear algebra, probability, and statistics preferred

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 Engineer 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

  • Complete improvements/experiments pertaining to customer issues.

The First 60 Days 

  • Regularly monitor and analyze the performance of deployed machine learning models

  • Work with ML Maintenance team to prioritize and resolve customer issues

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 Technical Assessment: You will meet with a potential future coworker to really dive into the role, the team, expectations, and what success means at Flock. You should expect the interview will cover the hard skills required to do the job, an architectural discussion, and an exploration of your past accomplishments and experiences. 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. You will meet with several teammates and cross-functional partners. Engineers should expect a technical assessment through coding interviews or architecture discussions. Your recruiter will provide more details to ensure you are fully prepared for your big day. 

  4. The Executive Review: A chance to meet a leader within the Machine Learning team. They will be looking to get to know you better, understand your motivations, and understand your technical background. 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 between $117,000 and $171,000 as well as Flock Safety 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.

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

$144000 / YEARLY (est.)
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$117000K
$171000K

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 Machine Learning Engineer II - Maintenance, Flock Safety

Are you ready to take your Machine Learning expertise to the next level? At Flock Safety, we are looking for a talented Machine Learning Engineer II to join our Maintenance Team. Flock is on a mission to eliminate crime and enhance community safety through innovative technology solutions. As part of our dynamic team, you will play a crucial role in optimizing and enhancing the machine learning models that power our services. You will support the entire machine learning model lifecycle, from developing algorithms to addressing model performance and drift. Your efforts will directly contribute to reducing crime rates and improving community safety across the nation. We value a culture of collaboration and innovation, and you'll find a supportive environment where your contributions are recognized. Flock Safety is backed by significant venture capital and offers a career-defining experience, all while ensuring you have a fulfilling work-life balance. With a flexible remote work policy and a range of employee benefits, including generous parental leave and health care coverage, we strive to ensure that our employees feel valued and supported every day. If you're passionate about using cutting-edge technology to make a real impact, we would love to hear from you!

Frequently Asked Questions (FAQs) for Machine Learning Engineer II - Maintenance Role at Flock Safety
What responsibilities does a Machine Learning Engineer II have at Flock Safety?

As a Machine Learning Engineer II at Flock Safety, you will support the entire machine learning model lifecycle, focusing on improving model performance and addressing model drift. You'll be working closely with various stakeholders, analyzing deployed models, and resolving customer issues, ensuring our technology effectively eliminates crime and promotes public safety.

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What qualifications are needed for the Machine Learning Engineer II position at Flock Safety?

To be a successful candidate for the Machine Learning Engineer II role at Flock Safety, a BS or MS in Computer Science, Mathematics, Physics, Engineering, or equivalent experience is required. Additionally, you'll need hands-on experience with frameworks like TensorFlow, PyTorch, and a good grasp of deep learning and traditional ML algorithms. Proficiency in Python, SQL, and basic Git knowledge will also be essential for the role.

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How does Flock Safety support the career growth of a Machine Learning Engineer II?

Flock Safety prioritizes career growth and offers a collaborative environment to support your development as a Machine Learning Engineer II. Regular performance monitoring and integration with key stakeholders will help you gain valuable insights and experiences that will propel your career forward while working on impactful projects.

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

At Flock Safety, the work culture is results-oriented and inclusive. As a Machine Learning Engineer II, you'll work within a supportive team that values collaboration and diverse perspectives. The company promotes a flexible remote work policy, ensuring strong team connections and a fulfilling work-life balance, essential for any incoming engineer.

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What is the salary range for the Machine Learning Engineer II role at Flock Safety?

The starting salary for a Machine Learning Engineer II at Flock Safety ranges from $117,000 to $171,000, based on your job-related experience and education. This competitive compensation package also includes stock options and various benefits that contribute to your overall job satisfaction and financial well-being.

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Common Interview Questions for Machine Learning Engineer II - Maintenance
Can you explain your experience with machine learning frameworks such as TensorFlow or PyTorch?

In your response, share specific projects where you've successfully utilized TensorFlow or PyTorch to implement machine learning models. Discuss the challenges you faced and how you overcame them, demonstrating a clear understanding of the importance of these frameworks in developing and deploying effective ML solutions.

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How do you approach solving model drift issues in deployed machine learning models?

Explain your methodology for monitoring model performance post-deployment. Discuss techniques for detecting drift, your strategies for retraining models, and how you communicate these processes with stakeholders to maintain the effectiveness of machine learning applications in real-world contexts.

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What methodologies do you use to communicate complex machine learning concepts to non-technical stakeholders?

It’s valuable to showcase your ability to translate technical jargon into comprehensible language. Discuss specific instances where you presented your findings to non-technical teams and emphasize the importance of understanding their perspectives in navigating project goals and addressing concerns.

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Describe a complex problem you solved using machine learning techniques.

Provide a detailed example of a significant problem you tackled, including the steps you took to gather data, select the right algorithms, and evaluate your outcomes. Highlight the impact your solution had on the project, emphasizing your critical thinking skills throughout the process.

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

Discuss your time management techniques and how you assess the urgency and importance of each project. Share any tools or frameworks you employ to organize tasks effectively, ensuring all projects move forward efficiently.

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What experience do you have with SQL and integrating data for model training?

Relate your proficiency in SQL to data retrieval and manipulation tasks you've undertaken for model training. Discuss a specific scenario where you used SQL to support ML efforts, describing the data you handled and how it affected your model’s performance.

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Can you discuss your understanding of supervised versus unsupervised learning?

Provide clear definitions and practical examples of both supervised and unsupervised learning. Discuss scenarios wherein one approach would be more appropriate than the other and your experiences applying both techniques throughout your career.

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

Share your methods for remaining informed, such as attending conferences, participating in online courses, and engaging with community forums. Emphasize the importance of continuous learning in the ever-evolving field of machine learning.

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How would you handle a situation where a deployed model underperformed?

Discuss your action plan for diagnosing performance issues, including data analysis and model evaluation. Highlight your experience in implementing corrective measures and the importance of continuous monitoring to ensure ongoing model effectiveness.

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What are your strategies for iterating on machine learning models to improve their performance?

Describe the iterative process you follow, discussing your approach to model evaluation, gathering new insights, and applying feedback. Share specific case studies to illustrate how this iterative method has led to successful improvements in your previous projects.

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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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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
April 17, 2025

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