Principal ML Engineer job in Austin, TX
Are you a trailblazing technologist passionate about building next-generation machine learning systems? We are looking for a Principal Machine Learning Engineer to join an innovative team in Austin, TX. This is a unique opportunity to lead high-impact AI initiatives, architect scalable ML solutions, and shape the future of intelligent systems. If you thrive at the intersection of research and real-world application, this role is made for you.
Drive Innovation in Machine Learning at the Heart of Austin’s Tech Scene
A leading technology-driven company in Austin, TX is seeking a Principal Machine Learning Engineer to spearhead its machine learning and AI strategy. The ideal candidate will bring deep expertise in ML model development, experience with cloud infrastructure, and a strong foundation in data science. You’ll play a central role in solving complex problems, mentoring top talent, and delivering solutions that power real-world products and services. Join a company that values curiosity, experimentation, and technical excellence.
Key Responsibilities of the Principal Machine Learning Engineer – Austin, TX
ML Strategy & Vision:
Define and drive the long-term strategy for machine learning initiatives across the organization. Align ML goals with business objectives, identify opportunities for AI-driven innovation, and ensure successful integration of intelligent systems into products and services.
Model Development & Deployment:
Design, develop, train, and optimize state-of-the-art machine learning models using structured and unstructured data. Oversee model deployment in production environments with scalability, efficiency, and performance in mind.
End-to-End ML Pipeline Management:
Lead the development and maintenance of robust ML pipelines—from data collection and preprocessing to model training, validation, and deployment. Ensure reproducibility, scalability, and monitoring throughout the entire machine learning lifecycle.
Cross-Functional Collaboration:
Partner with data engineers, product managers, software developers, and stakeholders to define use cases, scope projects, and deliver impactful ML solutions. Communicate complex technical concepts to non-technical teams effectively.
Research & Innovation:
Stay at the forefront of emerging ML technologies, frameworks, and algorithms. Experiment with cutting-edge methods (e.g., deep learning, reinforcement learning, generative models) and evaluate their applicability to business needs.
Code Quality & Best Practices:
Champion best practices in ML engineering, including code modularity, testing, documentation, and version control. Promote clean, maintainable, and production-grade code in both research and deployment environments.
Mentorship & Technical Leadership:
Mentor junior engineers and data scientists by providing technical guidance and fostering a culture of continuous learning. Set technical standards, conduct code reviews, and lead architecture discussions within the ML team.
Data Governance & Ethics:
Ensure the responsible use of data and models by applying fairness, transparency, and accountability principles in ML workflows. Address bias, privacy, and ethical considerations proactively.
Performance Monitoring & Optimization:
Implement tools for model evaluation, monitoring, and performance tuning post-deployment. Use metrics and analytics to continually assess and improve ML system accuracy, reliability, and efficiency.
Scalability & Cloud Integration:
Utilize cloud services (e.g., AWS, GCP, Azure) for model training, storage, and deployment. Architect scalable infrastructure that supports growing data and model complexity while maintaining low latency and high throughput.
What the Client is Looking for in You
As the Principal Machine Learning Engineer, the client is seeking a highly skilled and visionary technologist with a deep understanding of machine learning systems, scalable architecture, and AI-driven innovation. You should be a hands-on problem-solver who thrives in complex technical environments and is capable of driving both the strategy and execution of machine learning initiatives across the organization.
Proven Expertise in Machine Learning & AI
The ideal candidate brings a strong foundation in machine learning, artificial intelligence, and data science, with demonstrated success in building and deploying large-scale ML models. You should be proficient in modern ML frameworks (TensorFlow, PyTorch, Scikit-learn) and have experience applying ML in production environments across real-world applications.
Strategic Technical Leadership
The client is looking for a forward-thinking engineer who can contribute to the strategic direction of the ML organization. You must have the ability to connect long-term vision with practical execution, identifying emerging technologies and aligning ML investments with business goals. Experience in leading ML roadmaps, system design, and scalable architecture is essential.
Hands-On Engineering Skills
Beyond theoretical knowledge, you should be comfortable coding, debugging, and optimizing systems end-to-end. Mastery of Python and familiarity with cloud platforms like AWS, GCP, or Azure is critical. Experience in deploying ML models via APIs, containers (Docker/Kubernetes), and CI/CD pipelines is highly valued.
Data-Driven and Research-Oriented Mindset
You should possess strong analytical and research capabilities, with a passion for experimentation and innovation. The client values engineers who use data to validate models, track performance, and refine algorithms iteratively. A background in statistics, deep learning, or natural language processing is a plus.
Mentorship and Team Collaboration
As a senior-level engineer, your ability to mentor junior engineers and collaborate across teams is crucial. You should be an effective communicator who can articulate complex ML concepts clearly to both technical and non-technical stakeholders. The client values team players who uplift others and contribute to a collaborative, inclusive work environment.
Commitment to Responsible AI and Model Ethics
The client is also seeking someone who understands the ethical implications of machine learning. Experience with fairness, transparency, explainability, and privacy in ML models is highly desirable. You should be an advocate for building responsible, unbiased, and trustworthy AI systems.
Track Record of Impactful Delivery
Ultimately, the client is looking for someone who can build and ship ML systems that make a measurable impact. Your portfolio should include successfully launched models or projects that have added significant value to products, services, or user experiences.
FAQs About the Role – Principal Machine Learning Engineer – Austin, TX
1. What are the key responsibilities of the Principal Machine Learning Engineer in this role?
As a Principal Machine Learning Engineer, you will lead the design, development, and deployment of advanced machine learning models. You’ll architect end-to-end ML solutions, manage scalable pipelines, mentor junior engineers, and collaborate cross-functionally to integrate ML into real-world applications. Additionally, you’ll be responsible for driving innovation, ensuring model reliability, and contributing to strategic AI initiatives within the organization.
2. What qualifications and experience are required for this position?
The ideal candidate should have 7+ years of experience in machine learning, data science, or a related field, with a proven track record of building and deploying production-grade ML models. Strong proficiency in Python, ML frameworks (e.g., TensorFlow, PyTorch), and cloud platforms (AWS, GCP, or Azure) is essential. A Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related discipline is highly preferred.
3. What technical skills are essential for this role?
Expertise in supervised and unsupervised learning, deep learning, model evaluation techniques, and ML lifecycle management is crucial. Experience with MLOps tools, data engineering workflows, and API integration for serving ML models is highly valued. The role also requires strong coding skills, system design knowledge, and the ability to write production-level software.
4. What challenges can I expect in this role?
You’ll face challenges related to scaling machine learning systems, handling large and diverse datasets, and ensuring model performance in real-time environments. Staying current with rapid AI advancements, addressing ethical considerations, and deploying models in complex, distributed systems will also be part of the role. Balancing experimentation with delivery timelines is a key challenge.
5. What is the expected impact of the Principal ML Engineer on the organization?
You are expected to drive technical excellence and deliver machine learning solutions that directly impact product functionality, user experience, and business performance. Your contributions will shape the company’s AI strategy, improve operational efficiency, and create data-driven innovations that give the company a competitive edge.
6. What is the company’s culture and work environment like?
The company promotes a culture of curiosity, innovation, and collaboration. Teams are cross-functional, agile, and focused on solving real-world problems through advanced technology. You’ll work in a supportive environment that encourages continuous learning, open communication, and experimentation, with opportunities to contribute to both strategic direction and hands-on development.
What Remuneration Can You Expect from This Job?
As a Principal Machine Learning Engineer based in Austin, TX, you can expect a highly competitive compensation package reflective of your senior-level technical expertise and leadership in the field of artificial intelligence. The package typically includes the following components:
1. Base Salary
The base salary for a Principal ML Engineer varies depending on experience, company size, and project complexity. In Austin’s tech-driven market, you can expect an annual base salary ranging from $160,000 to $220,000, with top-tier tech companies potentially offering even higher.
2. Performance-Based Bonuses
In addition to a fixed salary, most compensation plans include annual performance-based bonuses. These bonuses are typically tied to project success, innovation milestones, and company performance. Bonus payouts can range from 10% to 30% (or more) of your base salary.
3. Equity & Stock Options
To align your success with the company’s growth, many organizations offer equity compensation, including RSUs (Restricted Stock Units) or stock options. Especially in startups or high-growth tech firms, equity can represent a significant portion of total compensation and offer long-term wealth-building opportunities.
4. Long-Term Incentives & Retention Awards
Some companies also provide long-term incentive plans (LTIPs) or retention bonuses, rewarding contributions to strategic goals over several years. These may be tied to innovation benchmarks, leadership impact, or company valuation milestones.
5. Benefits & Perks
Principal-level engineers often receive comprehensive benefit packages, which may include:
Health, dental, and vision insurance
401(k) plan with employer match
Paid time off and flexible work schedules
Remote/hybrid work options
Professional development stipends
Access to wellness programs and mental health support
Commuter benefits or travel allowances
Top-tier equipment and work-from-home support
6. Signing Bonuses & Relocation Assistance
To attract top engineering talent, especially from other markets, companies may offer signing bonuses or relocation support. Signing bonuses can range from $10,000 to $50,000, depending on the candidate’s experience and company policy.
Total Compensation Potential
When you combine base salary, bonuses, equity, and benefits, the total annual compensation for a Principal Machine Learning Engineer in a high-growth or well-funded company can range from $200,000 to $400,000+, with even greater upside depending on stock performance or startup equity appreciation.
How to Apply
If you’re a passionate and experienced Machine Learning professional ready to lead innovative projects and contribute to high-impact AI solutions, we invite you to apply for the Principal Machine Learning Engineer position in Austin, TX. This is an exciting opportunity to work at the forefront of technology, shape scalable ML systems, and drive measurable business value.
To apply, please submit your updated resume along with a cover letter that highlights your experience in:
Developing and deploying machine learning models at scale
Leading data science or ML engineering teams
Building production-grade ML pipelines and APIs
Working with cloud platforms (AWS, GCP, Azure) and MLOps tools
Solving complex problems with deep learning, NLP, or recommendation systems
Showcase your ability to bridge the gap between research and engineering, mentor teams, and deliver machine learning products that make an impact.
This role offers an opportunity to join a forward-thinking, innovation-driven company and lead projects that shape the future of AI-driven applications. Apply today to take your ML engineering career to the next level.
For more information or to explore similar AI & Data Science roles, visit our Machine Learning Engineer Recruiters Page.
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Principal Machine Learning Engineer | AI Careers | Machine Learning Jobs | MLOps | Data Science | Deep Learning | NLP | Scalable ML Systems | Cloud AI | AI Leadership | Python | TensorFlow | PyTorch | ML Engineering Austin
