Senior Applied ML Engineer

Raise - find a more meaningful working experience; Austin (TX); On-site; USD 144,648 - 206,640; Full time

Job summary

Raise is looking for a Senior Applied ML Engineer in Austin, TX, or Atlanta, GA. This role involves contributing to machine learning solutions at scale, executing hands-on engineering, and mentoring others.

The candidate must possess 2–4 years of ML engineering experience, a strong knowledge of ML algorithms, and proficiency in Python and relevant ML frameworks. The position offers a dynamic environment with a hybrid work model.

Qualifications

• 2–4 years of relevant ML engineering experience.
• Solid knowledge of ML algorithms like clustering and neural networks.
• Hands-on experience with ML frameworks and tooling.

Responsibilities

• Build secure, reliable, and scalable ML features.
• Conduct stress testing to validate production readiness.
• Participate in internal communities and external learning forums.

Skills

ML engineering experience

ML algorithms knowledge

Python proficiency

SQL skills

Experience with ML frameworks

Cloud ML platform experience

Experience in Linux/Unix environments

Familiarity with REST APIs

Tools

TensorFlow

Pandas

PyTorch

Jupyter Notebooks

Scikit-learn

Job description

Senior Applied ML Engineer

• Pay Rate: $105.00 - $150.00/hour on W2 (Rate based on experience and interview performance)
• Location: Austin, TX or Atlanta, GA (Hybrid 2-3 days onsite)
• Contract Length: 1+ Years

Must be authorized to work in the United States. We are unable to sponsor or take over sponsorship of an employment visa at this time. This role is for direct W2, no C2C (corp to corp) available.

About the Role

We are hiring a Senior Applied ML Engineer to join a product‑focused team building machine learning solutions at scale. In this role, you will contribute hands‑on engineering alongside mentorship and technical leadership. You are expected to work independently, influence technical decisions, and help shape the direction of ML systems across the product.

Senior engineers here are strong practitioners first. You will pair frequently with teammates, conduct code reviews, and set a high standard for quality and maintainability.

Responsibilities

• Delivery & Execution (approx. 70% of time)
  • Build secure, reliable, and scalable ML features as a core member of a cross‑functional product team
  • Ensure quality and change control standards are consistently met; document systems and processes thoroughly
  • Write automation scripts for infrastructure, monitoring, and test coverage
  • Conduct stress and resilience testing to validate production readiness
  • Adapt off‑the‑shelf tools and platforms to fit evolving requirements
  • Create instrumentation including dashboards, alerts, and logging to enable proactive operations
• Continuous Learning (approx. 10% of time)
  • Participate in internal communities of practice and external learning forums
  • Independently research emerging ML techniques and tooling to inform team decisions
• Support & Enablement (approx. 20% of time)
  • Serve as a resource for partner teams and support functions
  • Monitor production systems and maintain awareness of Service Level Objectives
  • Regularly assess system capacity, prediction quality, and overall production health

Qualifications

• 2–4 years of relevant ML engineering experience
• Solid working knowledge of ML algorithms including clustering, forecasting, anomaly detection, and neural networks
• Practical experience with regression and foundational statistics
• Hands‑on use of ML frameworks and tooling: Jupyter Notebooks, Pandas, SciPy, Scikit‑learn, Gensim, TensorFlow, PyTorch
• Experience with a major cloud ML platform (e.g., Vertex AI, BigQueryML) and data engineering tools such as BigQuery
• Proficiency in Python; experience with modern web frameworks (Node.js) and front‑end technologies (HTML, CSS, JavaScript, React, D3)
• Experience with GPU acceleration (CUDA, cuDNN)
• Strong SQL skills and experience with relational databases
• Proficiency with Git and CI/CD workflows
• Experience working in Linux/Unix environments
• Experience designing and consuming REST APIs
• Familiarity with production systems architecture including availability, failover, and security
• Familiarity with NoSQL databases
• Familiarity with cloud automation patterns and managed ML services
• Familiarity with defensive coding and high‑availability patterns
• Exposure to A/B testing and scalable web service design
• Familiarity with advanced ML techniques such as NLP, convolutional neural networks, autoencoders, and embeddings

Core Competencies

• Operates independently with minimal direction
• Navigates complexity and ambiguity with confidence
• Communicates clearly in technical and cross‑functional settings
• Brings innovative thinking to product and technical challenges
• Strong collaborator who actively supports team success
• Drives results consistently; holds self to a high standard