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Freelance Machine Learning Engineer Jobs in Detroit, MI

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director of AI Engineering, you'll contribute to the development of cutting-edge AI solutions to combat ...

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director of AI Engineering, you'll contribute to the development of cutting-edge AI solutions to combat ...

In order to set you up for success as a Machine Learning Engineer at Wayve, we're looking for the following skills and experience. Essential * Extensive and proven track record of shipping deep ...

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Freelance Machine Learning Engineer information

See Detroit, MI salary details

$14

$47

$130

How much do freelance machine learning engineer jobs pay per hour?

As of Aug 5, 2026, the average hourly pay for freelance machine learning engineer in Detroit, MI is $47.23, according to ZipRecruiter salary data. Most workers in this role earn between $24.04 and $61.15 per hour, depending on experience, location, and employer.

What does a freelance machine learning engineer do?

A Freelance Machine Learning Engineer designs, develops, and implements machine learning models and algorithms for clients on a project basis. They work independently to analyze data, build predictive models, and help businesses solve complex problems using AI and machine learning techniques. Their responsibilities may also include data preprocessing, model evaluation, and deploying solutions into production environments. Freelance Machine Learning Engineers often collaborate remotely with teams and must manage their own schedules and client relationships.

What are the key skills and qualifications needed to thrive as a freelance machine learning engineer?

To thrive as a Freelance Machine Learning Engineer, you need expertise in programming (especially Python), a solid grasp of machine learning algorithms, and a relevant academic background such as a degree in computer science, mathematics, or engineering. Familiarity with frameworks like TensorFlow or PyTorch, cloud platforms (AWS, GCP, Azure), and experience with version control systems are typically required. Strong problem-solving, self-management, and client communication skills help set successful freelancers apart. These competencies are crucial for delivering effective solutions, managing projects independently, and building client trust in a competitive market.

How do freelance machine learning engineers typically manage client expectations and project scopes?

Freelance machine learning engineers often work with clients who may not have a deep technical understanding of AI or data science. A common challenge is clearly defining the project scope and deliverables at the outset, ensuring both parties understand what is feasible given the data, time, and budget constraints. Successful freelancers use regular progress updates, milestone-based deliverables, and transparent communication to manage expectations and avoid scope creep. Building trust through clear documentation and setting realistic timelines also helps foster long-term client relationships.

What is the difference between Freelance Machine Learning Engineer vs Data Scientist?

AspectFreelance Machine Learning EngineerData Scientist
CredentialsTypically requires a degree in computer science, data science, or related fields; certifications in machine learning or AI are a plusUsually holds a degree in statistics, data science, or related areas; certifications in data analysis or visualization are common
Work EnvironmentIndependent, project-based work often remotely for various clientsOften employed full-time in organizations or consulting roles, sometimes freelance
Industry UsageUsed across tech, finance, healthcare, and startups for deploying ML modelsApplied in research, analytics, and strategic decision-making across industries

Freelance Machine Learning Engineers focus on developing and deploying ML models independently for diverse clients, while Data Scientists analyze data to extract insights, often working within organizations. Both roles require strong technical skills, but their work scope and environment differ significantly.

What are the most commonly searched types of Machine Learning Engineer jobs in Detroit, MI? The most popular types of Machine Learning Engineer jobs in Detroit, MI are:
What job categories do people searching Freelance Machine Learning Engineer jobs in Detroit, MI look for? The top searched job categories for Freelance Machine Learning Engineer jobs in Detroit, MI are:
What cities near Detroit, MI are hiring for Freelance Machine Learning Engineer jobs? Cities near Detroit, MI with the most Freelance Machine Learning Engineer job openings:
Infographic showing various Freelance Machine Learning Engineer job openings in Detroit, MI as of June 2026, with employment types broken down into 89% Full Time, 8% Part Time, and 3% Contract. Highlights an 78% Physical, 2% Hybrid, and 20% Remote job distribution, with an average salary of $98,234 per year, or $47.2 per hour.

Senior Machine Learning Engineer

Canopy

Detroit, MI • Remote

$126K - $180K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 10 days ago


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Job description

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director of AI Engineering, you’ll contribute to the development of cutting-edge AI solutions to combat vehicle and content theft. In this senior role, you’ll play a pivotal part in shaping our AI roadmap, mentoring junior engineers, and influencing system architecture decisions. This is a high-impact role with visibility across engineering and product leadership.

Responsibilities:
  • Contribute to the design, development, and deployment of robust machine learning models for production use in real-world security applications.
  • Develop within the full machine learning lifecycle; from problem definition to data pipeline design, model development, validation, deployment, and monitoring.
  • Establish and refine best practices in our ML system architecture, CI/CD pipelines for ML, and reproducible research methodologies.
  • Collaborate with cross-functional stakeholders including product managers, data engineers, and MLOps teams to ensure seamless model integration and delivery.
  • Perform advanced exploratory data analysis on large-scale sensory datasets (image, audio, radar, accelerometer) to derive insights and guide modeling strategies.
  • Stay ahead of industry advancements in machine learning, AI sensing, and signal processing, incorporating the latest innovations into Canopy’s technology stack.
  • Mentor and guide junior engineers and contribute to the hiring process and technical reviews.

Requirements

  • 5+ years of professional experience developing and implementing ML for perception systems with expertise in at least one of either RADAR, camera, or LiDAR.
  • Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
  • Expertise in Python with extensive experience in at least one deep learning framework (PyTorch or TensorFlow.
  • Proven ability to develop production-grade ML applications for training, evaluation and inference on large-scale datasets.
  • Experience creating C/C++ applications utilizing modern language features and build systems, preferably for porting ML inference applications from Python to edge devices/embedded systems.
  • White-box understanding of classical ML algorithms (SVMs, HMMs, Decision Trees) and modern neural network models and architectures (CNNs, transformers) with significant experience applying them for perception systems.
  • Experience implementing and applying dynamic object tracking, with experience using sensor fusion as a preference.
  • Proficiency in Unix-based environments (Linux, macOS) including working with remote servers and services, virtual computers and clusters.
  • Proficiency in signal processing techniques such as time/frequency-domain processing (e.g. Fourier Transform), filtering, and noise reduction.
Preferred Qualifications:
  • Experience in deploying models to edge hardware, including experience with PyTorch and ONNX and model compression techniques, e.g. quantisation and pruning.
  • Experience using cloud computing platforms, e.g., AWS or GCP.
  • Experience with MATLAB for algorithm prototyping and research.
  • Experience with Docker or containerisation.
  • Reside within the Detroit area or nearby, with the ability to work in a hybrid environment and regularly commute to our Detroit office as needed.

Benefits

  • Comprehensive medical benefits coverage, dental plans and vision coverage.
  • Health care and dependent care spending accounts.
  • Employee and Family Assistance Program (EAP).
  • Employee discount programs.
  • Retirement plan with a generous company match.
  • Generous Paid Time Off, Sick, and Holidays
  • Family Leave (Maternity, Paternity)
  • Short- and long-term disability
  • Life insurance and accidental death & dismemberment insurance

Compensation Range
Compensation may vary depending on skills and experience.
Base Salary: $126,000 - $180,000


Diversity, Equity and Inclusion: At Canopy, we're on a mission to end theft from vehicles and revolutionize vehicle security by building cutting-edge technology. We will achieve this by prioritizing individuals and staying attuned to the evolving needs of our people, users, and industry trends. We foster a workplace culture that embraces diversity and authenticity, enabling us to flourish as a team of exceptional individuals working towards a common purpose. We gain a deeper understanding of our users' experiences by continuously improving our skills and expanding our knowledge. A more diverse, equitable, and inclusive Canopy leads to greater innovation and success.


Equal Opportunity: Canopy does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.


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