Machine Learning Engineer

Machine Learning Engineer at Built In NYC — Research Report

Introduction

The Machine Learning Engineer role at Built In NYC offers a unique pathway into the rapidly evolving field of artificial intelligence, specifically within the healthcare and technology sectors. This position is not just about coding; it is about bridging the gap between complex research and real-world production systems that impact patient outcomes. While the specific deadline for the internship is not mentioned in the general listing, candidates should be aware that similar roles, such as the June 2026 Machine Learning Intern, often have strict application windows, typically closing in early February. Securing this role provides a significant career boost, offering hands-on experience with cutting-edge technologies like Large Language Models (LLMs) and transformer architectures while working alongside industry leaders.

Overview of Built In NYC

Built In NYC is a premier digital platform that connects top talent with the most innovative companies in New York City, serving as a critical hub for the local tech ecosystem. Unlike traditional job boards, Built In NYC functions as a comprehensive resource for company culture insights, salary data, and career development, positioning itself as a niche leader in the tech recruitment and information industry. The platform’s key product is its extensive database of company profiles, which includes detailed information on work environments, benefits, and growth trajectories for hundreds of New York-based tech firms. Its market presence is dominant within the New York tech community, with a reputation for transparency and high-quality data that attracts both employers seeking visibility and job seekers looking for authentic insights.

The culture at Built In NYC is dynamic, fast-paced, and deeply rooted in the collaborative spirit of the New York tech scene. Employees and partners often describe the work environment as inclusive and forward-thinking, where innovation is prioritized over rigid hierarchy. People want to work here because it offers a front-row seat to the most exciting developments in the city’s tech industry, providing unparalleled networking opportunities and access to a vast network of industry professionals. For a Machine Learning Engineer, this environment is particularly valuable as it fosters cross-disciplinary collaboration and encourages the adoption of emerging AI technologies.

Machine Learning Engineer Role

Role Overview

A Machine Learning Engineer at a company featured on Built In NYC, such as Phamily (a healthcare AI company), is responsible for the end-to-end development of AI-driven solutions that directly improve healthcare operations. Day-to-day, the intern or employee designs, prototypes, and tests novel machine learning approaches, focusing heavily on Natural Language Processing (NLP) and LLMs for healthcare use cases. The impact of this role is profound, as the models developed are not just theoretical but are deployed into production systems that influence patient care and operational efficiency. This role requires a unique blend of research skills and engineering pragmatism, ensuring that cutting-edge AI is translated into scalable, reliable software.

Detailed Responsibilities

The core responsibilities of this role are diverse and require a high level of technical proficiency and creativity. Key tasks include:

• Designing and prototyping novel ML approaches, especially in NLP, LLMs, and transformer architectures for healthcare use cases.
• Conducting applied research through rigorous experimentation, evaluation, and iterative model refinement.
• Developing sophisticated prompting strategies, fine-tuning techniques, and retrieval workflows to optimize model performance.
• Translating complex research findings into scalable, production-oriented systems that can handle real-world data loads.
• Building robust evaluation frameworks that connect model performance metrics directly to healthcare outcomes and patient safety.
• Collaborating closely with engineering and product teams to deploy AI-powered features into live environments.
• Working with large, messy, real-world healthcare datasets to derive actionable insights and improve model accuracy.
• Documenting methodologies, technical findings, and recommendations to ensure knowledge sharing and project continuity.

Day-to-Day Workflow

A typical day for a Machine Learning Engineer in this role involves a mix of deep research, coding, and team collaboration. The morning often starts with reviewing the latest experimental results from the previous day, analyzing model performance metrics, and identifying areas for improvement. This is followed by several hours of focused coding, where the engineer implements new algorithms, fine-tunes models, or develops data pipelines using Python and frameworks like PyTorch or TensorFlow. Mid-day usually includes team meetings with the Director of AI and engineering teams to discuss project progress, align on technical goals, and address any blockers. The afternoon is dedicated to experimentation, where the engineer runs new tests, validates hypotheses, and iterates on model designs. The day often ends with documenting findings and preparing for the next cycle of development.

Tools and Technologies

The technical stack for this role is modern and comprehensive, reflecting the latest advancements in AI and machine learning. Engineers primarily use Python as their core programming language, leveraging its extensive libraries for data science and AI. The deep learning frameworks of choice are PyTorch and TensorFlow, which are essential for building and training complex neural networks. For NLP and LLM tasks, the team heavily relies on Hugging Face, a platform that provides pre-trained models and tools for transformer architectures. Additionally, engineers work with large-scale data processing tools and may utilize cloud platforms like AWS, Azure, or GCP for deploying models and managing compute resources. The environment also includes tools for version control (Git), experiment tracking, and containerization (Docker, Kubernetes) to ensure scalable and reproducible deployments.

Skills and Requirements

Technical Skills

To succeed in this role, candidates must possess a robust set of technical skills that span deep learning, data engineering, and software development. A strong background in deep learning is non-negotiable, with specific expertise in NLP and LLMs being highly valued. Hands-on experience with PyTorch, TensorFlow, and Hugging Face is essential for building and training models. Candidates must also demonstrate proven ability to run complex experiments and derive meaningful insights from data, showing a deep understanding of statistical methods and model evaluation. Solid Python skills are required, along with the comfort of working with real-world, messy datasets that often lack the cleanliness of academic benchmarks. Furthermore, an interest in bridging the gap between research and production impact is a critical technical mindset.

Soft Skills

Beyond technical prowess, soft skills are equally important for a Machine Learning Engineer in this collaborative environment. Effective communication is vital, as engineers must explain complex technical concepts to non-technical stakeholders, including product managers and healthcare professionals. Strong teamwork abilities are necessary to work closely with the Director of AI, engineering teams, and product teams, ensuring that AI features are integrated smoothly into the broader product ecosystem. Problem-solving skills are critical for tackling the unique challenges of healthcare data, where accuracy and reliability are paramount. Additionally, adaptability and a willingness to learn are essential, as the field of AI is rapidly evolving, and new technologies and methodologies emerge frequently.

Experience Expectations

The experience expectations for this role are tailored to high-caliber candidates, typically targeting MS or PhD students in Machine Learning, Computer Science, or related fields. A strong academic background is expected, with a focus on coursework and projects related to deep learning, NLP, and AI. Candidates should have a proven portfolio of projects that demonstrate their ability to build, evaluate, and improve machine learning systems. While a graduate degree is preferred, exceptional candidates with a Bachelor’s degree and significant relevant experience may also be considered. The role values practical experience over pure academic theory, so candidates with prior internships or work experience in AI solution development are highly favored. A solid GPA is often a baseline requirement, but the quality of the portfolio and the depth of technical understanding are the primary factors in the selection process.

Salary and Benefits

For a Machine Learning Engineer internship in New York, the compensation is competitive and reflects the high demand for AI talent in the region. Based on similar roles in the area, such as the Machine Learning Intern at Phamily, the hourly rate typically ranges from $30.00 to $35.00 per hour. For full-time Machine Learning Engineer positions in New York, the annual salary can range from $148,000 to $168,900, with some growth-stage AI companies offering base salaries between $165,000 and $350,000 plus equity. Internships often include additional perks such as a stipend for commuting or housing, especially for remote or hybrid roles. Benefits for full-time employees typically include paid public holidays, learning and development training budgets, and access to cutting-edge AI tools and resources. The role is also eligible for select benefits like paid sick leave and health insurance, making it a comprehensive opportunity for career growth.

Built In NYC Hiring Process

Step-by-Step Hiring Stages

The hiring process for a Machine Learning Engineer role at a company featured on Built In NYC is structured and rigorous, designed to identify the most qualified candidates. The process typically follows these five stages:

1. Application: Submitting a tailored resume, portfolio, and any required application forms through the Built In NYC platform or the company’s career site.
2. Screening: An initial review by the recruiting team or an automated system (ATS) to assess basic qualifications, experience, and alignment with the role’s requirements.
3. Assignment: Completing a technical assignment or project, such as building a small ML model, analyzing a dataset, or solving a coding problem, to demonstrate practical skills.
4. Interviews: Participating in one or more interview rounds, including technical interviews, behavioral interviews, and potentially a panel interview with the engineering team.
5. Offer: Receiving a formal job offer, including details on salary, benefits, and start date, followed by the negotiation and signing of the employment contract.

Application Timeline

The application timeline for these roles can vary depending on the specific company and the type of position (internship vs. full-time). For internships, such as the June 2026 Machine Learning Intern, the application window often opens in late fall and closes in early February, with the last day to apply typically being February 5, 2026. Full-time roles may have more flexible timelines, but candidates should be prepared for a process that can take several weeks to a few months. It is advisable to apply early, as many companies review applications as they are received and may fill positions before the official deadline. The typical process duration from application to offer is around 4 to 8 weeks, depending on the complexity of the role and the number of candidates.

Screening Methods

The screening methods used by companies on Built In NYC are designed to efficiently filter candidates and identify those with the most relevant skills. The initial screening often involves an ATS (Applicant Tracking System) that scans resumes for specific keywords related to machine learning, NLP, Python, and deep learning. Candidates must ensure their resumes include these keywords to pass the automated filter. Additionally, the screening process may require a portfolio review, where recruiters assess the quality and relevance of past projects, GitHub repositories, or research papers. Some companies may also conduct a brief phone screening to verify basic qualifications and assess communication skills. The goal of the screening is to create a shortlist of highly qualified candidates who are ready for the more in-depth technical and behavioral interviews.

Interview Preparation

Example Interview Questions

Interviews for a Machine Learning Engineer role at a company featured on Built In NYC will likely include a mix of technical and behavioral questions. Here are four realistic questions candidates might encounter:

• “How would you design a machine learning model to predict patient outcomes using unstructured healthcare data, such as doctor’s notes?”
• “Explain the difference between fine-tuning a pre-trained LLM and training a model from scratch. When would you choose one over the other?”
• “Describe a challenging project where you had to work with messy, real-world data. How did you handle the data quality issues, and what was the outcome?”
• “How do you ensure that your machine learning models are not only accurate but also fair and unbiased, especially in a healthcare context?”

How to Answer

When answering these interview questions, candidates should use structured strategies like the STAR method (Situation, Task, Action, Result) to provide clear and concise responses. For technical questions, it is important to explain the reasoning behind your approach, not just the final answer. Start by describing the situation or task, then detail the specific actions you took, and conclude with the results and impact of your work. For behavioral questions, focus on demonstrating your problem-solving skills, teamwork, and adaptability. Be prepared to discuss your past experiences in detail, including any challenges you faced and how you overcame them. Additionally, show your enthusiasm for the field of AI and your willingness to learn and grow, as these are key traits that recruiters look for in Machine Learning Engineers.

What Recruiters Evaluate

Recruiters evaluating candidates for a Machine Learning Engineer role are looking for a combination of technical expertise, practical experience, and cultural fit. Key hiring criteria include a strong understanding of deep learning and NLP, proven ability to work with real-world datasets, and experience with PyTorch or TensorFlow. Recruiters also assess the candidate’s ability to translate research into production, as this is a critical skill for the role. Behavioral traits such as communication, teamwork, and problem-solving are equally important, as the role requires close collaboration with various teams. Finally, recruiters look for candidates who demonstrate a genuine passion for AI and a commitment to ethical and fair machine learning practices, especially in the healthcare sector.

How to Get Selected

Practical Tips

To get selected for a Machine Learning Engineer role at a company featured on Built In NYC, candidates should take several practical steps. First, tailor your resume and portfolio to highlight your experience with NLP, LLMs, and healthcare data, as these are the most relevant skills for the role. Ensure your resume includes specific keywords that will pass the ATS screening, such as “PyTorch,” “TensorFlow,” and “deep learning.” Second, build a strong portfolio of projects that demonstrate your ability to build, evaluate, and deploy machine learning models. Include detailed documentation and code samples to showcase your technical skills. Third, network with professionals in the field by attending industry events, joining online communities, and connecting with recruiters on platforms like LinkedIn. Finally, prepare thoroughly for the interview by practicing technical questions and reviewing your past projects, so you can confidently discuss your experience and insights.

Common Mistakes to Avoid

Candidates often get rejected for Machine Learning Engineer roles due to several common mistakes. One major mistake is submitting a generic resume that does not highlight the specific skills and experiences relevant to the role. Another mistake is failing to demonstrate practical experience with real-world data, as recruiters value candidates who can handle messy, unstructured datasets. Additionally, some candidates struggle to explain their technical approach clearly, which can make it difficult for recruiters to assess their skills. Another common mistake is neglecting to prepare for behavioral questions, which can lead to a poor impression of the candidate’s teamwork and communication abilities. Finally, some candidates fail to show enthusiasm for the field of AI or a commitment to ethical practices, which can be a red flag for recruiters in the healthcare sector.

How to Stand Out

To stand out in the competitive field of Machine Learning Engineering, candidates should take advanced strategies that go beyond the basics. One effective strategy is to build a portfolio that includes not just standard projects but also innovative solutions that address real-world challenges, such as improving patient outcomes or optimizing healthcare operations. Another strategy is to engage in networking by connecting with industry professionals, attending conferences, and participating in online communities to build relationships and gain insights. Additionally, candidates should submit tailored applications that specifically address the company’s needs and show how their skills align with the role’s requirements. Finally, demonstrating a commitment to ethical AI and fairness in machine learning can make a candidate more appealing to recruiters, especially in the healthcare sector, where these issues are of paramount importance.

Final Thoughts

The Machine Learning Engineer role at a company featured on Built In NYC is a transformative opportunity for anyone passionate about AI and its potential to improve healthcare. By combining cutting-edge research with practical engineering, this role offers a unique chance to make a real impact on patient outcomes and operational efficiency. Whether you are an intern looking to gain hands-on experience or a full-time engineer seeking to advance your career, this position provides the tools, resources, and support needed to succeed. Don’t wait to apply; the deadline for many roles is strict, and the competition is fierce. Take the first step today by tailoring your resume, building your portfolio, and connecting with the industry. Your future in AI starts now.

Frequently Asked Questions

Q: What is the salary for a Machine Learning Engineer at Built In NYC?
A: For internships, the hourly rate typically ranges from $30.00 to $35.00. For full-time positions in New York, the annual salary can range from $148,000 to $168,900, with some companies offering up to $350,000 plus equity.

Q: How competitive is it to get hired at Built In NYC?
A: The role is highly competitive, as it requires a strong background in deep learning, NLP, and LLMs, along with practical experience with real-world data. Candidates with MS or PhD degrees and a proven portfolio are most favored.

Q: What skills are most important for this role?
A: The most important skills include expertise in deep learning, NLP, and LLMs, hands-on experience with PyTorch and TensorFlow, and the ability to work with messy, real-world healthcare datasets. Communication and teamwork are also critical for success.