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Senior Staff Machine Learning Engineer, Multitask Modeling

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. Flock’s computer vision products decode information to search against using several deep learning multitask models with a shared backbone and multiple task heads. As a Sr. Staff ML Scientist, you will lead the research and development of Flock’s multitask deep learning models.

The Skillset

  • PhD or MS in Computer Science, Electrical Engineering, or a related field with 10+ years of experience in computer vision and deep learning.

  • Proven experience improving multitask learning architectures, particularly with shared backbones and multiple task heads.

  • Strong proficiency in PyTorch, TensorFlow, or JAX, with a track record of improving, extending, and training deep learning models

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

  • Good software engineering skills in Python

  • Basic SQL knowledge

  • Basic Git knowledge

  • Basic Bash knowledge

Feeling uneasy that you haven’t ticked every box? That’s okay; we’ve felt that way too. Studies have shown women and minorities are less likely to apply unless they meet all qualifications. We encourage you to break the status quo and apply to roles that would make you excited to come to work every day.

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 ML Engineer with Flock Safety. 

The First 30 Days

  • Focus on onboarding and understanding the existing codebase, datasets, and infrastructure.

  • Set up a development environment and begin training a model.

  • Collaborate with cross-functional teams, including Machine Learning, Data Engineering, and Product, to gain insights into workflows and dependencies.

  • Contribute a small, impactful piece of code on your first day to kickstart engagement.

  • Review modeling and data systems to understand how they interact and support existing use cases.

  • Shadow the team to gain exposure to ongoing projects, maintenance tasks, and troubleshooting processes.

  • Generate a list of potential improvements in model performance, stability, or testability..

The First 60 Days

  • Begin extending enhancements and optimizing models for specific use cases.

  • Work independently with guidance, bringing forward potential solutions instead of just questions.

  • Improve training metrics and provide insights on potential refinements.

  • Conduct cross-validation experiments, analyze results, and refine data strategies.

  • Generate ideas for dataset improvements and initiate implementations.

  • Collaborate with senior team members to develop a list of experiments and enhancements, aiming to complete at least four.

90 Days & Beyond

  • Lead and drive advancements by deploying optimizations that improve model performance and usability.

  • Independently manage tasks with minimal oversight while maintaining transparency.

  • Contribute to improving the evaluation dataset and model comparison framework for deeper insights.

  • Review production monitoring systems and recommend improvements.

  • Enhance model deployment and inference services to optimize efficiency.

  • Engage in cross-team collaboration to refine strategies and drive impactful changes.

  • Contribute to knowledge-sharing initiatives, such as internal presentations or documentation enhancements.

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 Technical Assessment: Our technical assessments seek to test the hard skills required to do the job. Engineers may find themselves in coding interviews or architecture discussions, sales roles may present mock discovery calls, and leadership roles may craft 90 day plans. Your recruiter will inform you of which assessment you will be assigned and ensure you are fully prepared for your big day. Expect to defend all relevant design work in your resume.

  4. The Executive Review: A chance to meet an executive 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 $240,000-$310,000 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 Senior Staff Machine Learning Engineer, Multitask Modeling, Flock Safety

If you're a talented Senior Staff Machine Learning Engineer with a knack for multitask modeling, Flock Safety has an amazing opportunity waiting for you! At Flock, we create cutting-edge technology solutions designed to combat crime and enhance community safety. Our innovative platform combines various technologies and works hand-in-hand with cities, businesses, and law enforcement agencies. We’re looking for someone passionate about making a difference! As our Senior Staff Machine Learning Engineer, you will lead the research and development of intelligent multitask models, playing a pivotal role in how we interpret complex data sets to improve public safety. Your expertise in computer vision and deep learning will be invaluable as you enhance existing architectures and explore new possibilities with PyTorch, TensorFlow, or JAX. We value a collaborative environment where you can not only thrive but also make a positive impact on society. As Flock scales up, we aim to meet our ambitious goal of reducing crime in the U.S. by 25% over the next three years. Join our dynamic team where creativity meets technology and be part of a mission to create safer communities. We celebrate diversity and offer an inclusive workplace where new ideas are always welcome. With options for flexible working and a robust range of benefits, you can expect a fulfilling career at Flock Safety, where your work truly matters.

Frequently Asked Questions (FAQs) for Senior Staff Machine Learning Engineer, Multitask Modeling Role at Flock Safety
What responsibilities does a Senior Staff Machine Learning Engineer have at Flock Safety?

A Senior Staff Machine Learning Engineer at Flock Safety is responsible for leading the research and development of multitask deep learning models. This involves improving existing learning architectures, optimizing algorithms, and collaborating with cross-functional teams to understand workflows. Candidates will be expected to enhance model performance and usability, as well as contribute to knowledge-sharing initiatives within the organization.

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

To qualify for the Senior Staff Machine Learning Engineer role at Flock Safety, candidates should possess a PhD or MS in Computer Science, Electrical Engineering, or a related field, along with over 10 years of experience in deep learning and computer vision. Proven ability to work with multitask learning architectures and knowledge of frameworks like PyTorch or TensorFlow are also essential.

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

At Flock Safety, we foster a positive and inclusive work culture for our Senior Staff Machine Learning Engineers. Our team thrives on collaboration, rewarding proactive contributions, and promoting engagement between remote workers. We believe in supporting our employees while they make real impacts on community safety, emphasizing transparency and mutual respect in our work environment.

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What can I expect during the interview process for the Senior Staff Machine Learning Engineer role at Flock Safety?

The interview process for the Senior Staff Machine Learning Engineer position at Flock Safety is designed to reflect our culture of transparency and collaboration. You will have several stages, including initial chats with recruiters, technical assessments, and interviews with hiring managers. Each step will guide you, ensuring you feel prepared to showcase your expertise effectively.

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What is the salary range for a Senior Staff Machine Learning Engineer at Flock Safety?

The salary range for a Senior Staff Machine Learning Engineer at Flock Safety is competitive, falling between $240,000 and $310,000 depending on job-related experience, education, and market indicators. This position also includes the opportunity for stock options, enhancing the overall compensation package.

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Common Interview Questions for Senior Staff Machine Learning Engineer, Multitask Modeling
What experience do you have with multitask learning architectures?

When answering this question, highlight specific projects where you've improved multitask learning architectures. Discuss the techniques you used, challenges faced, and how your contributions led to tangible results.

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Can you describe a deep learning project you successfully led?

To tackle this question successfully, choose a project that demonstrates your leadership skills. Outline the project goals, your role, the technologies utilized, and the impact the project had on your team or organization.

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How do you stay updated with the latest developments in machine learning?

In your response, emphasize your passion for continuous learning. Mention specific resources you follow, such as research papers, online courses, or conferences, and how you implement new knowledge in your work.

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What programming languages and tools are you proficient in, relevant to this role?

List languages and tools such as Python, PyTorch, TensorFlow, or JAX. Explain how you've applied these in your previous roles, including any special projects or frameworks you've developed.

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How do you approach debugging and optimizing machine learning models?

Share your systematic approach to debugging and optimizing, including specific metrics you monitor, frameworks you utilize, and examples of improvements you’ve made in models from past experience.

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Discuss how you handle collaboration in a remote environment?

Highlights your experience with remote collaboration tools and strategies. Mention how you maintain communication, ensure clarity, and leverage technology to make collaboration effective.

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What do you consider when designing a deep learning model?

Your answer should cover aspects like data quality, model architecture, testing methods, and how you align your model design to meet specific problem-solving needs.

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Can you provide an example of a time you dealt with data imbalance in your projects?

Provide an instance where you've encountered data imbalance and explain the techniques you employed to mitigate its impact, such as using data augmentation, oversampling, or undersampling.

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How do you evaluate the performance of machine learning models?

Discuss specific evaluation metrics you use, such as accuracy, precision, recall, and F1 score. Explain how you determine which metrics to prioritize based on the project's goals.

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What are your thoughts on the ethical considerations of machine learning?

Express your awareness of ethical implications in AI and machine learning. Discuss how you ensure fairness, transparency, and the steps you take to minimize bias in your models.

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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
March 17, 2025

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