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Contract Machine Learning Startup Jobs in Massachusetts

Xometry

Staff Machine Learning Engineer

Waltham, MA

This includes defining the data contracts for model inputs/outputs and implementing the MLOps ... machine learning engineering, with a track record of owning and delivering complex ML systems in ...

Xometry

Staff Machine Learning Engineer

Waltham, MA · On-site

This includes defining the data contracts for model inputs/outputs and implementing the MLOps ... machine learning engineering, with a track record of owning and delivering complex ML systems in ...

Redhat

Machine Learning Engineer

Boston, MA · On-site +1

$136K - $225K/yr

We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the ...

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Contract Machine Learning Startup information

What are some common challenges faced by machine learning professionals working on a contract basis at startups?

Machine learning professionals working as contractors at startups often face challenges such as rapidly changing project scopes, limited access to large datasets, and the need to quickly adapt to new tools and frameworks. Startups typically move fast, so contractors must be comfortable with ambiguity and prioritize delivering value in short timeframes. Additionally, they may need to collaborate closely with cross-functional teams, such as product managers and engineers, to ensure that machine learning solutions align with business goals.

What is the difference between Contract Machine Learning Startup vs Data Scientist?

AspectContract Machine Learning StartupData Scientist
CredentialsRelevant degrees, certifications in ML/AITypically similar credentials, often with advanced degrees
Work EnvironmentProject-based, startup setting, flexible hoursOffice or remote, corporate or research settings
Employer & IndustryStartups in tech, AI, or data-driven sectorsVaried industries including tech, finance, healthcare
Search & Comparison IntentUnderstanding contract roles in ML startupsExploring data science career options

Contract Machine Learning Startup roles focus on short-term, project-based work within startup environments, often requiring specialized skills in ML and AI. Data Scientists typically work in more established companies or research settings, with similar credentials but often in a full-time capacity. Both roles demand strong technical backgrounds, but contract roles offer flexibility and varied projects, while Data Scientists may have more stability and broader responsibilities.

What are the key skills and qualifications needed to thrive in a Contract Machine Learning Startup role, and why are they important?

Success in a Contract Machine Learning Startup role generally requires expertise in machine learning algorithms, data analysis, and a solid background in computer science or related fields. Familiarity with programming languages such as Python or R, experience with ML frameworks like TensorFlow or PyTorch, and knowledge of cloud platforms (e.g., AWS, GCP) are typically expected. Strong problem-solving, adaptability, and effective communication help professionals collaborate with clients and respond to rapidly changing project requirements. These skills and qualities are vital to deliver innovative, scalable solutions in fast-paced, outcome-driven startup environments.

What is a Contract Machine Learning Startup?

A Contract Machine Learning Startup is a company or team that provides machine learning solutions and services to clients on a contract basis. Instead of developing their own products, these startups typically work with other businesses to build custom machine learning models, analyze data, and help integrate AI technologies into existing workflows. They may offer expertise in areas such as natural language processing, computer vision, or predictive analytics, and usually operate on short-term or project-based contracts. This approach allows client companies to access specialized knowledge without hiring full-time data scientists or engineers.
What are the most commonly searched types of Machine Learning Startup jobs in Massachusetts? The most popular types of Machine Learning Startup jobs in Massachusetts are:
What are popular job titles related to Contract Machine Learning Startup jobs in Massachusetts? For Contract Machine Learning Startup jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Contract Machine Learning Startup jobs in Massachusetts look for? The top searched job categories for Contract Machine Learning Startup jobs in Massachusetts are:
What cities in Massachusetts are hiring for Contract Machine Learning Startup jobs? Cities in Massachusetts with the most Contract Machine Learning Startup job openings:
Contract Machine Learning Startup jobs near you
Infographic showing various Contract Machine Learning Startup job openings in Massachusetts as of July 2026, with employment types broken down into 6% Internship, 67% Full Time, 14% Part Time, 7% Temporary, and 6% Nights. Highlights an 93% In-person, and 7% Remote job distribution.
Staff Machine Learning Engineer, Technical Lead

Staff Machine Learning Engineer, Technical Lead

Paperless Parts

Boston, MA • On-site

Full-time

Posted 13 days ago

Job description

Job Summary:
Paperless Parts is a SaaS startup helping manufacturers quote faster and win more work. They are seeking a Staff Machine Learning Engineer, Technical Lead to drive R&D execution and lead the technical efforts in building the core manufacturing intelligence engine. This role involves mentoring early-career engineers and operationalizing machine learning infrastructure to solve complex manufacturing challenges.
Responsibilities:
• Drive R&D Execution: Own planning and execution of the AI/ML pod’s backlog. Partner closely with the Chief Scientist and other engineering pods to ensure the research pipeline aligns smoothly with border product timelines.
• Prototype and Transition: Lead the hands-on prototyping of novel solutions and transition of successful proofs-of-concept into production-ready services. Guide the strategic migration of workloads, identifying opportunities to shift repetitive tasks from expensive frontier models to fine-tuned, open-source architectures.
• Operationalize ML Infrastructure: Develop scalable, repeatable approaches to labeling data, training models, and deploying services that support our products with AI capabilities.
• Design Rigorous Benchmarks: Define and track metrics that evaluate the effectiveness and costs of our AI-powered solutions, enabling key technology decisions to be data-driven.
• Mentor the Pod: Act as the technical anchor and primary mentor for early-career ML engineers. Cultivate an engineering culture of deep theoretical and practical rigor through hands-on pairing and comprehensive design and code reviews.
Qualifications:
Required:
• 8+ years of experience in relevant R&D roles with a strong background in SaaS products at scale
• Advanced Academic Foundation: a technical degree in Computer Science, Applied Mathematics, or closely related field, with a strong understanding of the mathematics behind modern AI/ML techniques is essential.
• AI/ML Fundamentals: A robust understanding of core machine learning and deep learning theory, including neural networks, statistical modeling and inference, and metric learning.
• MLOps: Experience working with cloud-native patterns for ML pipelines, including platforms like AWS SageMaker.
• Communication Mastery: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and influence decisions without relying on authority.
Preferred:
• start-up to scale-up transition experience preferred
• An advanced degree and track record of peer-reviewed publications is a strong plus when paired with proven software experience in industry.
Company:
Paperless Parts is a manufacturing intelligence company building a new type of marketplace for custom parts. Founded in 2017, the company is headquartered in Boston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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