Machine Learning Engineer How to Hire, Salary Data, and Job Descriptions
A machine learning engineer uses their computer programming skills as well as coding skills in order to obtain data for processing and analyzing. Machine learning engineers develop algorithms and other predictive forms to organize this data through machine learning. For example, when an online chatbot is being utilized by a particular company, it sorts through data using algorithms that were created by machine learning engineers in order to properly respond to the online customer.
Machine learning engineers work in a variety of industries as technology is extremely widespread these days. They develop artificial intelligence and machine learning algorithms that power gadgets, such as Siri, Alexa, online chatbots, marketing bots, and much more.
Sample job description #1
Our team operates at the intersection of building highly scalable applications, analytics to understand data content and big data machine learning to improve the quality of the ecosystem. Be ready to make something great when you come here. Dynamic, inspiring people, and innovative, industry-defining technologies are the norms at ABC Company. The people who work here have reinvented and defined entire industries with our products and services. The same passion for innovation also applies to our business practices – strengthening our commitment to leave the world better than we found it.
Our tech stack:
C++ (LLVM) for program analysis
Python (TensorFlow/PyTorch/Keras) for ML research and production
Java for backend
Spark for offline data processing
Cassandra/PostgreSQL for data storage
Docker and Kubernetes as internal cloud infrastructure
We don’t expect prior experience with all the technologies involved, but you will be expected to learn to use them effectively. In this role you will be working together with both ML engineers and engineers owning all the components of our stack, operations, and product teams to build content analysis systems and integrate them into various business workflows.
The ability to reverse engineer binary executables
Familiar with C, C++, Obj-C, and Swift internals
Familiar with Linux/iOS/macOS internals
Static/dynamic binary instrumentation experience
Solid Java programming skill
Machine learning knowledge
Strong communication skills, particularly written communication
Nice to have
Experience with deep learning frameworks (TensorFlow/PyTorch/Keras)
Experience with common deep learning models (YOLO, BERT, etc.)
Experience with building end-to-end Machine Learning system
Experience with Big Data processing framework like Hadoop/MapReduce/Spark
Experience using CI/CD environments (Jenkins, Spinnaker, etc)
Designing and developing program analysis and machine learning software for analyzing media contents, including apps, images, videos, and audios
Developing functionalities using machine learning such as classification, information extraction, clustering, and topic modeling
Collaborate with cross functional teams of engineers, data analytics, machine learning experts, and products to build new features
Education & experience
BS in engineering, computer science or other technical disciplines, plus 5 years of related experience.
Sample job description #2
We are seeking machine learning research engineers to join our data science team. We are a fast growing, ambitious, and collaborative team with the mandate to identify areas within ABC Company where machine learning will improve our investment outcomes. We use machine learning methodologies to drive value throughout the investment pipeline, on problems like trade execution, portfolio construction, and signal generation. A successful candidate will collaborate with our research scientists, senior investors, and platform engineers on model building, deployment, and scaling. The individual will work on projects requiring complex machine learning models with high investment value and difficult engineering challenges throughout the stack from infrastructure, platform to algorithmic, and visualization development. A successful candidate will have the opportunity to own broad swathes of investment research and participate actively in the idea generation and the productization of research.
We think you’ll click with us if you:
Are passionate for understanding and applying the latest machine-learning techniques
Can drive complex, high-value ML projects involving multiple partners to success, on time
Act as an owner, assume responsibility, and drive issues to resolution
Constantly raise the bar, exploring new, unique solutions to established and emerging issues
Anticipate problems and complications, and formulate solutions to unblock the progress of the project
Can balance pragmatism, excellence, and agility with good logic
Can prioritize and organize many concurrent threads across different initiatives whilst balancing risk
You’ll drive the following responsibilities:
Developing and validating models to answer complex investment questions
Creating and deploying new ML systems and experiments in a production environment
Partnering with senior investors to ensure alignment with Bridgewater’s investment understanding
Collaborating with engineers to drive productionization of new methodologies
Excellent quantitative background in machine learning, statistics, and experimental design
Proven track-record of building ML training and inference pipelines with unique ML models for regression problems
Strong knowledge of the latest ML statistical techniques and ability to pragmatically apply to problems
Capability to efficiently deploy and scale validated models
Degree from an accredited undergraduate institution with excellent academic track-record in Computer Science, Statistics, or related field. Advanced degree is preferred
Exceptional experience in R/Python with particularly track-record writing performant and maintainable code
Working knowledge of causal inference and C/C++ preferred, but not required
Sample job description #3
Performs deep dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision making.
Develops queries and performs extensive programming to access, transform, and prepare data for statistical modeling
Performs deep dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision making
Identifies and diagnoses data inconsistencies and errors, documents data assumptions, and forages to fill data gaps
Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses. Brings structure to requests and translates requirements into an analytic approach
Guides test design, research design, and model validation. Provides statistical consultation services. Serves as the analytics expert on cross functional teams for large strategic initiatives and contributes to the growth of the Vanguard analytic community
Prepares and delivers insight presentations and action recommendations. Communicates sophisticated analytical findings and implications to business partners
Participates in special projects and performs other duties as assigned
Minimum of eight years data analytics, programming, database administration, or data management experience
Undergraduate degree or equivalent combination of training and experience. Graduate degree is preferred
Average salary and compensation
The average salary for a machine learning engineer is $142,000 per year in the United States and an average of $155,450 with a yearly bonus included. Salary will vary based on location, company size, education, and level of experience.
Los Angeles, California
New York City, New York
Sample interview questions
What does recall mean in this field of work?
What algorithm do you prefer to use, and why that one?
Can you tell us the differences between variance and bias?
Why do you want to work for us?
What makes you stand out from other candidates?
What skill sets do you think are the most important for a machine learning engineer?
What does precision mean in this line of work?
How can you avoid overfitting?
What different types of machine learning algorithms can you name?
In which case would you use PCA?
How would you explain cross-validation?
Can you explain linear regression?
Technology is always changing. How do you keep up with the latest technology?
How proficient would you say you are with machine learning tools?
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