PragmatikePrincipal AI/ML Engineer
Pragmatike is hiring a Principal AI/ML Engineer for a remote role open to applicants in United States. Level rates it ; you can apply on Pragmatike.
Level's read on this job
- On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include PyTorch, TensorFlow and scikit-learn.
- Level classifies it as a lead role.
- Level lists 51 other open roles at Pragmatike.
AI in this role
Location: Cambridge, MA (Eastern Time / UTC -4) Relocation package available
Relocation package: Available
Start date: ASAP
Languages: English (required)
About the Role
Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers.
We are looking for a Senior AI/ML Engineer to join a high-performing AI engineering team building and deploying real-world AI systems used by Fortune 500 customers. You will take technical ownership of critical ML components, lead model development and productionization efforts, and work closely with AI researchers, platform engineers, and product teams to deliver scalable, production-grade AI systems.
This role is intended for experienced AI/ML engineers who have already built and deployed models in production environments and want to operate at the intersection of research-quality AI and real-world systems engineering.
What You’ll Do
Design, build, train, and optimize machine learning and deep learning models for production use
Lead the deployment of ML models into scalable, reliable production systems
Architect training, inference, and evaluation pipelines for structured and unstructured data
Own model performance, reliability, scalability, and lifecycle management
Drive experimentation, model iteration, and performance benchmarking
Implement and maintain high-quality ML codebases primarily in Python
Monitor, debug, and improve live production models
Collaborate with platform, backend, and infrastructure teams on system integration
Contribute to technical direction, architecture decisions, and AI roadmap planning
Mentor junior engineers and support team-level technical excellence
What We’re Looking For
Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Mathematics, or a related field
7+ years of professional experience in AI/ML, data science, or applied machine learning roles
Proven experience deploying ML systems into production environments
Strong understanding of ML fundamentals, evaluation methodologies, and model lifecycle management
Deep hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
Strong proficiency in Python
Experience designing scalable ML systems and pipelines
Ability to operate independently and take technical ownership of complex systems
Strong problem-solving, architecture, and system-design skills
Bonus Points
Experience with deep learning, LLMs, GenAI, or modern AI architectures
Experience building AI systems used by enterprise or large-scale customers
Strong experience with cloud platforms (AWS, GCP, Azure)
MLOps experience (CI/CD for ML, model deployment, monitoring, observability)
Research background, publications, or applied AI innovation projects
Experience leading AI initiatives or mentoring ML engineers
Why This Role Is High-Impact
Work directly with MIT CSAIL–founded leadership
Build AI systems deployed to Fortune 500 customers
Operate at the intersection of cutting-edge AI and real-world production systems
High ownership and influence over technical direction
Join a well-funded, fast-growing AI startup with strong technical ambition
Clear path toward Staff/Principal AI Engineer or AI Architect roles
Benefits
Competitive salary & equity options
Sign-on bonus
Health, Dental, and Vision
401(k)
Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.
How we rate this
Principal AI/ML Engineer at Pragmatike rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- Typical machine learning engineer job
- $200k
This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through how you've used PyTorch in your day-to-day work.
- What are the limits of TensorFlow that you've run into, and how did you work around them?
- What's a project where you used scikit-learn hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: ML Ops, PyTorch, TensorFlow, and scikit-learn. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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