Machine Learning Engineer Jobs in the United States
SewerAI Corp
Walnut Creek, CA
We are looking for a machine learning engineer to join our team who wants to work on our entire machine learning pipeline that helps SewerAI deliver real-time insights to cities around the world and to ourselves. Strong knowledge of machine learning techniques and tools, experience with both classical techniques and modern approaches (gradient boosted trees, neural networks).
Exact Sciences Corp
San Diego, CA
This role utilizes working knowledge of advanced artificial intelligence and machine learning algorithms and models to solve problems involving biological, genomic, clinical and healthcare data within a setting of advanced cancer screening and precision oncology. This position contributes to the strategic vision for the application of cutting-edge AI methodologies at Exact Sciences and works as a partner with biostatisticians, bioinformatics scientists, and data scientists to further our goal of helping eradicate cancer by preventing it, detecting it earlier, and guiding personalized treatment.
Money Fit by DRS
Columbia, MD
Maverc Technologies is a proven and effective small business partner and consultant, recognized as a leader in providing cyber security and IT services to the Federal, State, and local Government and within the Intelligence Community. In this role, you and your team will be responsible for the entire lifecycle of machine learning models, from managing and deploying them to troubleshooting any pipeline issues that arise.
Money Fit by DRS
San Mateo, CA
Train various deep learning models on different GPUs, including multi-GPU setups, and improve model performance and resource utilization by fine-tuning hyperparameters and using advanced techniques like LoRA and QLoRA quantization. You will be responsible for designing and building decentralized and distributed AI training pipelines, optimizing training for fast performance and low costs, and conducting research on cutting edge techniques for training on heterogeneous environments.
Money Fit by DRS
Not Available, CA
We are building a platform and infrastructure for Conversational AI Voice Agents so that every business, developer, or tinkerer can easily build talking human-like AI agents and use them to serve their customers; this will unlock massive value for the world and a lot of happiness for people using these delightful agents. We are in search of Full-time Machine Learning Engineers and Researchers who are passionate about solving challenging problems and inventing the future of how people interact with LLMs.
Money Fit by DRS
San Francisco, CA
You'll get to drive key product initiatives end to end and work alongside exceptionally strong co-workers who learn quickly and ship quality work. We have reached a pivotal stage in our journey: After launching ourknowledge assistant for work in July, we're now growing our customer base and product rapidly.
Money Fit by DRS
Mountain View, CA
Join the dynamic team at Kumo Experiences and play a pivotal role for launching new product features to deliver a world class experience for performing machine learning over relational databases. Our platform enables creating state-of-the-art models without the need for intricate and manual feature engineering, by harnessing the power of Graph Neural Networks which automatically learn features on large scale data.
Money Fit by DRS
Dallas, TX
Unlike tools that only provide correlation, only they provide true causation, giving organizations across sector and industry the ability to quantify the value of every marketing activity and maximize future marketing investments. Partner across all teams including engineers, AI scientists, researchers, designers, and product managers to bring new features into production.
Money Fit by DRS
San Francisco, CA
AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We value engineers who are self-starters, care deeply about the end user experience, and take pride in building products to solve customer needs.
Machine Learning Engineer Jobs Overview
Machine learning engineer jobs are incredibly varied, and shaping our world in amazing ways. For example, behind those smart recommendations on our streaming apps, or that cool face recognition feature on our phones, lies the work of machine learning engineers.
Almost every industry needs machine learning engineers, and the demand will continue to rise. This is one of the best jobs in artificial intelligence. From tech giants like Google and Apple to healthcare, finance, entertainment, and even agriculture, ML engineers are making waves.
Common duties in a machine learning engineer job might include:
- Designing and implementing machine learning applications.
- Analyzing large and complex data sets to derive valuable insights.
- Collaborating with data scientists and other engineers to improve existing ML algorithms.
- Staying updated with the latest trends and research to enhance machine learning systems.
Common requirements for machine learning engineer jobs include:
- A bachelor’s degree in computer science, data science, or a related field. Some advanced roles require a master’s or PhD.
- Proficiency in programming languages.
- Deep understanding of machine learning algorithms.
- Strong analytical and problem-solving skills.
The top cities in the U.S. for machine learning engineer jobs are:
- New York
- San Francisco
- Atlanta
Salaries for Machine Learning Engineer Jobs
Machine learning engineer salaries range from $83,285 to $138,520 per year and greatly vary depending on the specific role, location, skills, and more. Use Monster’s Salary Tool to search and compare the average data engineer salary in any major U.S. location.
On top of a competitive salary, ML engineers often receive benefits such as medical insurance, 401(k), stocks, discounts, and more. So, don’t stop at salaries but be sure to compare the total compensation packages of jobs that interest you as well.
How to Find the Machine Learning Engineer Job That Fits You
Reflecting upon the following questions can give you clarity, ensuring you find not just a job, but the right job.
- What’s my passion within machine learning? Pinpointing your passion can guide you to specialized roles.
- Which industries intrigue me the most? Knowing where you want to make an impact can narrow down your options.
- How much do I value mentorship and guidance? Reflect on whether you would benefit more from a workplace that offers mentorship programs or prefer figuring things out on your own.
- Do I want to work on established ML products or build from scratch? Consider what excites you the most, refining existing machine learning models or pioneering new ones.
Pay Close Attention to the Job Description
Job descriptions are like treasure maps, pointing you to a role that could be your dream job -nor the contrary. But to reach that treasure you need to read the map carefully. Here’s why:
- To avoid application pitfalls. Some descriptions include specific instructions, like adding a keyword in your cover letter or attaching a particular project. Overlooking these details could send your application straight to the discard pile.
- To align your skills: Job titles can sometimes be deceiving. One company’s “ML engineer” might be another’s “data scientist”. A careful reading of the job description ensures the job matches your expertise and aspirations.
Research Companies, Not Just Roles
Understanding your potential employer matters. It’s where you’ll spend a significant chunk of your week, after all. Here are a few pointers on researching companies:
- Delve deep into a company’s recent publications, projects, or open-source contributions to get a clearer picture of their work.
- Read reviews from current and former employees to get insider perspectives about the company. A happy environment often leads to fulfilling work.
- Check their funding rounds, partnerships, or any recent news. You want to join a ship that’s sailing, not sinking.
How to Apply for Machine Learning Engineer Jobs
Before you take the plunge and click on that “Apply” button, let’s make sure your application stands out and truly showcases the ML superstar you are.
Revamp Your Resume
First, take note of repeated terms or tools in the job description likely central to the role. Then, customize your machine learning resume for the job by highlighting your relevant skills, projects, or coursework.
Shine a spotlight on your proficiency with tools and languages, be it Python, TensorFlow, PyTorch, or Scikit-Learn. Instead of vaguely mentioning that you “improved model performance”, be specific. Mention how you “enhanced model accuracy by 20% using ensemble methods.” Specificity speaks volumes.
Not confident about how your resume stacks up? Turn to Monster’s expert resume services for professional guidance.
Write a Cover Letter
A cover letter is like a window to your professional soul. More than just a formal introduction, it’s your chance to resonate with your potential employer on a more personal level.
- Personalize it and address the hiring manager by name.
- Describe your passion and share a short story or experience that ignited your interest in the field.
- Tailor it to the job by emphasizing your relevant experience, skills, or education.
- End strong by expressing excitement about potentially contributing to the company’s ML projects and offer a follow-up action, like a call.
Send Your Application
When applying for machine learning engineer jobs on Monster, remember these tips:
- Ensure the information and resume on your profile are correct and up to date.
- Set up alerts for machine learning engineer jobs near you to stay updated with the latest postings.
- Use this very page to find ML engineer jobs you can apply for directly through Monster.
- Keep track of your application history in your Monster account.
How to Follow Up on Your Job Application
Sent off that application and eagerly refreshing your inbox? Your excitement is understandable. Did you know that a thoughtful follow-up message can boost your chances? Check out these tips:
- The sweet spot for following up is typically one to two weeks post-application submission.
- Your best bet is to connect with the hiring manager or the person mentioned in the job listing.
- The most common and preferred follow-up method is to send an email. Keep it concise and professional.
- Start with a quick intro, reminding them of your application. Then, express your continued interest in the role and ask for updates on the status of your application.
Interviewing for Machine Learning Engineer Jobs
When interviewing for an ML engineer job, some of the questions will be about real-world challenges the company is facing. This will be your chance to showcase your practical skills and prove that you are the right person for the job. Use the STAR method to best structure your responses and create a story that shows what you are capable of.
Interview Questions for ML Engineer Jobs
To help you prepare, let’s take a peek at ten typical questions you might encounter in a machine learning engineer job interview:
- Describe a challenging problem you faced in a past ML project and how you overcame it. Interviewers want insights into your problem-solving skills and resilience. Be honest and highlight what you’ve learnt from experience.
- How do you handle missing or corrupted data in a dataset? Data is rarely perfect. This question gauges your experience with real-world data challenges. Mention why you might choose one method over another.
- Discuss a recent advancement in the field of machine-learning that has caught your attention and explain why you find it significant. This question assesses your passion and commitment to continuous learning in the evolving field of machine learning. Pick a topic you’re genuinely excited about.
- What steps would you take to ensure that a machine-learning model doesn’t perpetuate or amplify biases in the data? Ethical AI is crucial. This question tests your awareness of biases and fairness in ML. Mention the importance of diverse teams and continuous model assessment.
- How would you explain a complex machine learning model to a non-technical stakeholder or team member? Communication skills are vital in machine learning engineer jobs. Recruiters ask this question to verify your ability to translate technical jargon into understandable concepts.
How to Follow Up on Your Job Interviews
Given the dynamic nature of ML roles, a thoughtful follow-up after your job interviews can make all the difference. Check out these tips to have a bigger impact:
- Aim to follow-up within 24 to 48 hours post-interview.
- Start by expressing gratitude for the opportunity and recap a highlight or two from your conversation. If something genuinely piqued your interest during the interview, ask a follow-up question.
- If a week or two has passed without a word, and they haven’t given a specific timeline, it’s okay to send a gentle nudge. Frame it positively, expressing your continued enthusiasm for the role.
What to Do When You Get an Offer for a Machine Learning Engineer Job
Before you jump headlong into this next chapter, make sure you’re well-prepared to navigate this crucial phase.
- First things first, acknowledge the offer promptly. Whether you’re ready to give an answer or need a bit more time, let them know.
- Dive deep into the details, ensure you have a clear understanding of everything the job entails, and ask questions if you need clarity.
- Consider the entire package beyond salary and benefits. Opportunities to work on cutting-edge projects, learning, and growth can sometimes outweigh immediate monetary benefits.
- Once you’ve reviewed everything, regardless of whether it’s a yes or a no, communicate your decision respectfully and promptly.
Machine Learning Engineer Jobs Career Path
As a machine learning engineer, you’re at the forefront of one of the most transformative technologies of our era. But where can this road take you? ML is a vast and varied realm, with a myriad of opportunities to specialize, lead, and innovate. Let’s explore some career paths that you might consider as you shape your journey in this dynamic field:
- Robotics engineer: Designs, builds, and tests robots for various applications, ranging from manufacturing to exploration.
- Automation engineer: Designs, implements, and monitors systems that streamline and automate industrial processes, improving efficiency and product quality.
- Algorithm developer: Creates, tests, and optimizes algorithms that solve specific problems or perform particular tasks within software applications.
Machine Learning Engineer Jobs: Similar Occupations
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