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Machine Learning Engineer Jobs in West Lafayette, IN

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Python Tutor

West Lafayette, IN · Remote

$18 - $40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

... data science, engineering, and advanced mathematics. * Conceptual Teaching & Problem-Solving ... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction:

Statics Tutor

West Lafayette, IN · Remote

$18 - $40/hr

... machine design, and construction engineering. * Curriculum Awareness & Adaptive Instruction ... Ability to adapt to different learning styles and student needs. Ways To Connect With Students * 1 ...

CNC Consultant

Lafayette, IN · On-site

$70K - $89K/yr

Our always-on learning agenda drives their continuous improvement through building and transferring ... machining platforms. understanding of engineering drawings, tolerancing standards, and GD&T ...

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Machine Learning Engineer information

See West Lafayette, IN salary details

$30.3K

$123.9K

$186.2K

How much do machine learning engineer jobs pay per year?

As of Jul 29, 2026, the average yearly pay for machine learning engineer in West Lafayette, IN is $123,896.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,700.00 and $149,100.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are popular job titles related to Machine Learning Engineer jobs in West Lafayette, IN? For Machine Learning Engineer jobs in West Lafayette, IN, the most frequently searched job titles are:
What cities near West Lafayette, IN are hiring for Machine Learning Engineer jobs? Cities near West Lafayette, IN with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in West Lafayette, IN as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $123,896 per year, or $59.6 per hour.

Junior Machine Learning Engineer-remote

SynergisticIT

Lafayette, IN • On-site

Other

Posted 6 days ago


Job description

Your Degree Was Only the Beginning — Now It’s Time to Get Hired - Get Hired with a Process which Works ! A CS degree gives you a foundation, but employers want more — they want proof you can apply your knowledge in real‑world scenarios. SynergisticIT helps you bridge that gap. You’ll build industry‑level projects, sharpen your interview skills, and gain hands‑on experience in the technologies companies are hiring for right now. The program also markets your profile directly to Fortune 500 clients ,giving you visibility beyond what a degree alone can provide. If you want to turn your education into a real job offer, Synergisticit is the next step you need. If you’re getting interviews but not offers, you’re closer than you think—yet that final gap can feel brutal. Many candidates spend months learning frameworks and finishing courses, only to freeze during technical screens, system questions, or behavioral rounds. The result is painful: “almost hired” over and over again, while the confidence drops. The truth is that interviewing is its own skill, and Colleges don’t teach it. They teach how to code—but not how to think out loud, structure answers, debug in real time, defend trade-offs, and communicate like an engineer. Since 2010, SynergisticIT has helped candidates land full-time roles with many major employers. The best way to understand this: you can be smart and still fail interviews if you don’t know what the interview is truly measuring. Interviews rarely test “can you write code at home.” They test: Can you solve problems under constraints and time pressure? Can you communicate your approach clearly? Can you handle edge cases and complexity? Can you explain trade-offs and design choices? Can you show job-ready project depth, not just toy examples? SynergisticIT focuses on roles such as entry-level software programmers, Java full stack developers, Python/Java developers, Data Analysts, Data Engineers, Data Scientists, and Machine Learning Engineers. The focus areas include Java / Full Stack / DevOps and Data tracks like Data Engineering, Data Analytics/BI, ML/AI, because those are the roles employers continue to hire for. If your pattern is “I reach interviews but don’t clear them,” you likely need three upgrades: Stronger project narratives (what you built, why it matters, how it works) Stronger technical foundations (DSA, OOP, APIs, SQL, pipeline design) Mock interview reps (realistic simulation, feedback, improvement loops) Many jobseekers underestimate how much hiring is about clarity. You don’t need to be perfect—you need to show you can think, collaborate, and deliver. That’s why guided mock interviews and structured interview coaching can be a game-changer. Ideal candidates for this version include: Candidates who get interviews but repeatedly fall short Jobseekers stuck in “screen round limbo” Developers who panic during live coding Candidates who can build projects but struggle to explain them Professionals who haven’t interviewed in years and feel rusty Career changers who fear “I’m behind CS grads” (often untrue with support) If you’re tired of failing interviews and want a structured plan to convert interviews into offers, start here: please read our blogs · Why do Tech Companies not Hire recent Computer Science Graduates | https://www.synergisticit.com/why-tech-companies-dont-hire-recent-cs-graduates/ · Technical Skills or Experience? | Which one is important to get a Job? | https://www.synergisticit.com/tech-skill-or-experience-which-one-is-more-important-for-a-jobseeker/ Please check below links: Synergisticit Industry Event videos (OCW, JavaOne, Gartner data Analytics): https://fast.wistia.com/embed/channel/k4mlq69ekl USA Today feature Discover JOPP: https://www.synergisticit.com/jopp/ Contact: https://www.synergisticit.com/contact-us/ Because getting hired isn’t about trying harder—it’s about preparing smarter, practicing correctly, and having the right guidance. Please note: Resume databases are shared with clients and interested clients will reach out directly if they find a qualified candidate for their req. Resume submissions may be shared with our JOPP team database also. Please unsubscribe if contacted or if you don’t want to be contacted please don’t submit your resume.