Looking to hire Machine Learning Infrastructure Engineer in New York City? Explore our platform to find top-notch professionals to drive your business forward. Connect with us today!
Identify Your Needs: Determine the specific skills and expertise required for your data science, big data, machine learning, or AI project. HopHR specializes in these areas and can help you find the right talent.
Contact Us: We have a team of experienced recruiters and talent acquisition specialists who can assist you in finding the right candidate. HopHR has a fast-track talent pipeline and uses innovative talent acquisition technology, which can expedite the process of finding the right specialist for your needs.
Discuss Your Requirements: Have a detailed discussion with us about your company's needs, the nature of the project, and the qualifications required for the specialist. This will help us understand your specific requirements and tailor our search accordingly.
Review and Select Candidates: We will use our talent pool and recruitment expertise to present you with a selection of candidates. Review these candidates, conduct interviews, and select the one that best fits your project needs.
Submission-to-Interview Rate
Submission-to-Offer Ratio
Kick-Off to First Submission
Annual Data Hires per Client
Diverse Talent Percentage
Female Data Talent Placed
We are trusted by both startups and Fortune 500 companies, ensuring we can deliver top-tier Machine Learning Infrastructure Engineers for any business size.
Our unique approach is designed to provide actionable insights, helping clients make informed decisions when hiring Machine Learning Infrastructure Engineers.
We ensure the ideal job-talent fit each time, guaranteeing that the Machine Learning Infrastructure Engineers we provide will meet the specific needs of the client.
Our focus on both technical and soft skills in the recruitment process ensures that the Machine Learning Infrastructure Engineers we provide are well-rounded professionals.
Join over 100 startups and Fortune 500 companies that trust us
“I’ve used HopHR’s recruitment services as a hiring manager in two different companies. In my career, I’ve worked with a number of recruiters, but HopHR is in a class of its own when it comes to partnering in the process. They just “get it” and have consistently identified excellent global candidates for my positions at all levels of experience.”
“It’s been a great pleasure working with HopHR. Through their tireless efforts, we were able to make our first hire with them quite quickly. We made the hire out of 5 candidates we received on the very first talent batch. I would gladly recommend the usage of their services.”
“HopHR has been really able to identify our needs and consistently provide quality candidates. The data scientists we seek may not necessarily fit the typical profile, but HopHR has proven that they listen to our feedback and adjust searches to find the type of candidate we are looking for.”
“I have had an opportunity to engage with HopHR as a hiring manager in two different organizations. HopHR has a unique ability to quickly understand the needs of the roles and to provide high-caliber candidates, expertly navigates all stages of the candidate experience, making it easier to engage and to close out offers.”
Hiring a Machine Learning Infrastructure Engineer requires a keen eye for detail. Look for candidates with a strong background in computer science, data science, or a related field. They should have experience with machine learning algorithms, cloud services, and data structures. Proficiency in programming languages like Python, Java, or C++ is a must. Check their problem-solving skills and ability to work in a team. Don't forget to assess their understanding of data privacy and security. A great Machine Learning Infrastructure Engineer will not only have technical skills but also a passion for AI and its potential.
CVS Health, a US leader with 300K+ employees, advances America’s health and pioneers AI in healthcare.
AstraZeneca, a global pharmaceutical company with 60K+ staff, prioritizes innovative medicines & access.
HCSC, a customer-owned insurer, is impacting 15M lives with a commitment to diversity and innovation.
Clara Analytics is a leading InsurTech company that provides AI-powered solutions to the insurance industry.
NeuroID solves the Digital Identity Crisis by transforming how businesses detect and monitor digital identities.
Toyota Research Institute advances AI and robotics for safer, eco-friendly, and accessible vehicles as a Toyota subsidiary.
Vectra AI is a leading cybersecurity company that uses AI to detect and respond to cyberattacks in real-time.
BaseHealth, an analytics firm, boosts revenues and outcomes for health systems with a unique AI platform.
Yes, HopHR excels in high-volume quality sourcing with efficient candidate screening. Our platform streamlines the candidate identification and screening process, allowing mid-size companies to access a large pool of qualified candidates promptly and efficiently, outperforming traditional recruitment methods.
Look for a degree in Computer Science or related field, experience with machine learning algorithms, proficiency in Python or Java, and knowledge of data structures. They should understand cloud platforms like AWS, Azure, and have experience with big data tools like Hadoop, Spark. Familiarity with ML frameworks like TensorFlow is a plus.
HopHR stands out in sourcing talent for startups by employing cutting-edge talent search methods and technologies. Our unique sourcing strategies ensure startups find the best-fit candidates, offering a distinctive and effective approach to talent acquisition.
Ask about their past projects, the challenges they faced, and how they overcame them. Check their understanding of ML algorithms, cloud platforms, and data pipelines. Also, assess their skills in programming languages like Python, and tools like TensorFlow or PyTorch.
Post-fundraising, HopHR accelerates startup growth by providing targeted rapid scaling solutions. Through streamlined talent acquisition strategies, startups can swiftly enhance their data science capabilities to meet the demands of their expanding business landscape.
A Machine Learning Infrastructure Engineer should have experience in designing and implementing ML models, managing large datasets, developing scalable ML algorithms, and deploying these models into a production environment. They should also have worked on optimizing ML infrastructure and pipelines.
Mid-size companies should prioritize versatile analytics talent with expertise in data interpretation, machine learning, and business intelligence to meet specific mid-size company talent needs in the dynamic business environment.
Ask candidates to describe a complex ML project they've worked on, focusing on the problems they faced and how they solved them. Also, use technical tests or case studies related to ML infrastructure to assess their problem-solving skills and understanding of ML concepts.
HopHR seamlessly integrates with existing recruiting systems in large enterprises, offering enterprise hiring solutions that streamline the recruitment process. Our adaptable platform complements and enhances the functionality of current systems, ensuring a cohesive and efficient hiring strategy.
A Machine Learning Infrastructure Engineer should be proficient in Python, TensorFlow, PyTorch, Keras for ML models. They should also know Docker, Kubernetes for containerization, and AWS, Google Cloud, or Azure for cloud services. Familiarity with Hadoop, Spark, and Kafka for big data processing is also important.
Access top vetted diverse Talents. Accelerate your hiring process, reduce interviews, and ensure quality.
.png){kind=logo}
