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Senior Machine Learning Jobs in Austin, TX (NOW HIRING)

Roku

Senior Machine Learning Engineer, DevOps/SRE

Austin, TX · On-site

$128K - $165K/yr

... Machine Learning, Experimentation, and Inference Platform, which powers the entire landscape, and we continuously evolve. About the role We are seeking a talented and experienced Senior Software ...

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Senior Machine Learning information

See Austin, TX salary details

$24.8K

$79.6K

$162.1K

How much do senior machine learning jobs pay per year?

As of Sep 5, 2026, the average yearly pay for senior machine learning in Austin, TX is $79,581.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,100.00 and $102,100.00 per year, depending on experience, location, and employer.

What is a senior machine learning engineer?

Senior Machine Learning engineers are experienced professionals who design, develop, and deploy advanced machine learning models and systems. They typically lead projects, mentor junior team members, and collaborate with data scientists, software engineers, and stakeholders to solve complex problems using AI and data-driven techniques. Their responsibilities include researching new algorithms, optimizing model performance, and ensuring the scalability and reliability of machine learning solutions in production environments.

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

To thrive as a Senior Machine Learning Engineer, you need a strong background in mathematics, statistics, computer science, and experience with designing and deploying machine learning models, typically supported by an advanced degree in a related field. Proficiency with programming languages such as Python or R, ML frameworks like TensorFlow or PyTorch, and experience with cloud platforms and version control systems are commonly required. Excellent problem-solving skills, communication abilities, and leadership in collaborating with cross-functional teams make candidates stand out. These skills ensure the effective development, scaling, and integration of ML solutions to drive business value and innovation.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they address them?

Senior Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining model performance over time, and integrating models seamlessly with existing systems. Addressing these requires robust monitoring frameworks, collaboration with data engineering and DevOps teams, and implementing strategies like continuous integration/continuous deployment (CI/CD) for ML. Proactive communication with stakeholders and staying updated on the latest MLOps tools can also help ensure smooth deployment and ongoing reliability.

Is senior machine learning a high paying job?

Senior machine learning roles typically offer high salaries due to the specialized skills required, such as expertise in algorithms, programming, and data analysis. Compensation varies by industry, location, and experience, but these positions are generally among the higher-paying roles in tech and data science fields.

What are the most commonly searched types of Machine Learning jobs in Austin, TX?

The most popular types of Machine Learning jobs in Austin, TX are:

What are popular job titles related to Senior Machine Learning jobs in Austin, TX?

For Senior Machine Learning jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning jobs in Austin, TX look for?

The top searched job categories for Senior Machine Learning jobs in Austin, TX are:

Senior Machine Learning jobs near you
Infographic showing various Senior Machine Learning job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $79,581 per year, or $38.3 per hour.

Senior Machine Learning Engineer, Causal & Decision Systems

CSC Generation

Austin, TX • On-site

$58 - $75.75/hr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 21 days ago

Job description

CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs.
Reports to: CTO
Location: Hybrid- Austin, TX
About the Role
CSC Generation is building closed-loop decision systems that use machine learning to operate consumer businesses more intelligently. We are starting with pricing and expanding into areas such as inventory, purchasing, promotions, marketing, and assortment.
You will help build systems that estimate causal response and quantify uncertainty, choose actions, generate useful information, observe outcomes, update policies, evaluate challengers, and deploy within guardrails.
We want to answer questions such as:
  • What happens because we change a price, rather than simply what happens next?
  • How should uncertainty affect a decision?
  • When should the system exploit what it knows versus experiment to learn?
  • Can we estimate the value of a challenger policy before fully deploying it?
  • How do we optimize economic outcomes while respecting inventory, margin, vendor, customer, and operational constraints?

What You'll Do
Depending on your background, you may work across:
  • Causal and heterogeneous treatment-effect modeling
  • Uncertainty estimation and calibration
  • Contextual bandits, active learning, or sequential decision-making
  • Policy learning and constrained optimization
  • Counterfactual and off-policy evaluation
  • Experimentation and champion/challenger systems
  • Production ML infrastructure, monitoring, and automated deployment

We care about selecting the right method, not using a particular framework.
What Success Looks Like
Success is not a better offline metric.
The systems you build should produce measurable economic lift in controlled experiments, generalize across businesses, learn from their own interventions, and safely automate an increasing share of real commercial decisions.
Over time, the goal is simple:
the system should become better at operating the business because it has operated the business.
What We're Looking For
We care more about exceptional technical ability and judgment than matching a checklist. Strong candidates will have experience in several of:
  • Machine learning and statistical modeling
  • Causal inference and experimentation
  • Recommendation, advertising, pricing, marketplace, credit, or other decision systems
  • Bandits, reinforcement learning, optimization, or active learning
  • Uncertainty estimation
  • Counterfactual evaluation
  • Production ML systems
  • Python, SQL, and large behavioral datasets

Why This Role Is Different
Most ML systems learn from a dataset. Here, the decisions made by the model influence the data the model sees next. That creates a continuous loop: decision, intervention, outcome, learning, better decision.
The long-term opportunity is to build that capability once and apply it across a portfolio of businesses and increasingly broad commercial decisions.
  • Real-world impact. The systems you build will run live commercial decisions across a portfolio of consumer brands, so you will see measurable economic outcomes from your work, not just offline benchmark improvements.
  • Technical growth at the frontier. Causal decision systems that learn from their own interventions are still an open problem. You will work at the intersection of causal ML, bandit algorithms, and production engineering, with the latitude to choose the right method for the problem.
  • Full ownership. You will own problems end to end, from framing and modeling through production deployment and evaluation.
  • Competitive benefits. Comprehensive benefits including paid time off, 401(k) match, medical, dental, vision, supplemental coverage, and employee discounts across portfolio brands.

Interview Process
  1. Recruiter Screen: A conversation with our recruiting team to cover your background, the role, and mutual fit.
  2. Virtual Interview Rounds: Focused discussion with the hiring manager & deeper conversations with cross-functional engineering and data science collaborators covering technical depth, system design, and working style.
  3. In-Person Interview: A final on-site visit at our Austin office to meet the broader team and connect with key stakeholders.
  4. Reference Checks: Conducted in parallel with the final stages where possible.
  5. Offer: We move quickly for the right candidate.

Interview process is subject to change. Any updates will be shared promptly and clearly.
Please Note
  • Part of our interview process is a mandatory in-person interview with someone on our team prior to an offer. Candidates that are unwilling or unable to meet for an in-person interview will be removed from consideration immediately.
  • Due to a high volume of fraudulent applications, you must share a valid LinkedIn profile URL in the application questions below to be considered. If you do not have a LinkedIn profile, you must provide a credible reason in that field and supply alternative evidence of your professional background to verify your identity.

CSC Generation is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law.
The CSC Generation family of brands is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact [email protected].
For US-based candidates, this posting is intended for candidates that reside in the following states:
AZ, DE, FL, GA, IN, LA, MI, MS, MO, NV, NC, OK, PA, TN, TX, UT, WV, WI, and WY.
CSC Generation will conduct an exhaustive background check including verifying dates of employment directly with your former employer.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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About CSC Generation

Sourced by ZipRecruiter

CSC Generation is a multi-brand technology platform based in Merrillville, IN, United States. The organization operates in the retail sector and utilizes technology to save retail companies from going into bankruptcy, while also offering consumers the ability to lease their purchases. Founded by serial entrepreneur, Justin Yoshimura, CSC Generation has leveraged its proprietary technology and customer database to quickly revitalize distressed retail brands. The company's mission revolves around the concepts of reinvention and innovation as it aims to redefine traditional retail and direct-to-consumer models in today's digital age. Notably, the company has, to date, acquired several brands such as DirectBuy, Killion, and most notably, Z Gallerie, growing fast within the e-commerce sector.

Industry

Finance and insurance

Company size

501 - 1,000 Employees

Headquarters location

Merrillville, IN, US

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