Machine Learning Engineer Job Descriptions, Average Salary, Interview Questions

What Does a Machine Learning Engineer Do?

A machine learning engineer designs, builds, and maintains complex machine learning (ML) models and systems used in a variety of applications. Programming languages such as Python, Java, and R are required for the role, as well as knowledge of statistics and data analysis. In machine learning, algorithms and models are developed to enable computers to learn from data and to make predictions or decisions based on that learning. These developers also create and maintain the software infrastructure needed to support these models, including data storage and processing systems, APIs, and web services.

Due to the increasing use of machine learning and artificial intelligence in business, machine learning engineers are in high demand. Typically, they have degrees in computer science or a related field, as well as experience working with big data and machine learning algorithms.

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National Average Salary

Machine learning engineer salaries vary by experience, industry, organization size, and geography. To explore salary ranges by local market, please visit our sister site zengig.com.

The average U.S. salary for a Machine Learning Engineer is:

$142,370

Machine Learning Engineer Job Descriptions

Example 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.

Key Qualifications

  • 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)

Role responsibility

  • 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.

Example 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

Position requirements:

  • 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

Minimum qualifications:

  • 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

Example 3

Performs deep dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision making.

Core responsibilities

  • 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

Qualifications

  • 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

Sample Interview Questions

  • Why did you choose a career in machine learning?
  • Which programming languages and tools do you have experience with?
  • What do you do to stay up-to-date on machine learning?
  • What steps would you take to solve a machine learning problem?
  • Do you have experience with deep learning algorithms and neural networks?
  • When working on a machine learning project, what are some of the common challenges you face?
  • What measures do you take to ensure that machine learning models are accurate and reliable?
  • What is the difference between supervised and unsupervised learning?
  • Whenever you design a machine learning model, what ethical considerations do you consider?
  • Are you able to communicate the results of your machine learning projects to non-technical stakeholders?

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