Applied ML Engineer

Raise; Austin (TX); On-site; USD 110,208 - 158,424; Full time

Job summary

Raise is seeking a collaborative Applied ML Engineer to join a hybrid team in Austin, TX. You will drive ML features from design to deployment, collaborating with product managers, designers, and engineers.

This role emphasizes coding best practices, continuous learning, and effective collaboration within cross-functional teams. You must have 1–3 years of relevant experience and proficiency in Python.

The compensation ranges from $80.00 to $115.00 per hour, depending on experience.

Qualifications

• 1–3 years of hands-on experience in software engineering or ML development.
• Familiarity with cloud data stores such as BigQuery.
• Exposure to ML concepts including clustering and neural networks.

Responsibilities

• Partner with engineers and product managers to build ML features.
• Write and review production-quality code.
• Monitor production systems and track Service Level Objectives.

Skills

Python

Machine Learning

SQL

Web frameworks

Version control

Tools

Jupyter Notebooks

TensorFlow

Git

BigQuery

Job description

Applied ML Engineer

• Pay Rate: $80.00 - $115.00/hour on W2 (dependent upon 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.

Overview

We are looking for a collaborative, product-minded Applied ML Engineer to join a cross-functional delivery team. You will contribute to the full lifecycle of machine learning features — from algorithm design through to production deployment — working alongside product managers, UX designers, and software engineers to ship solutions that real users depend on.

This is a pairing-heavy environment. You will collaborate closely with teammates daily, contribute to code reviews, and help raise the bar on engineering practices across the team.

Responsibilities

• Delivery & Execution (approx. 65% of time)
  • Partner with engineers, product managers, and designers to build secure, reliable, and scalable ML-powered features
  • Write and review production-quality code; apply best practices and document your work to shared standards
  • Develop automation scripts for infrastructure provisioning, monitoring, and test execution
  • Build resilience tests to validate system behavior under stress conditions
  • Configure and adapt third-party tooling to meet changing business needs
  • Instrument dashboards, logging, and alerting to proactively catch and address issues in production
• Continuous Learning (approx. 15% of time)
  • Engage with internal and external communities of practice around ML and modern software development
  • Stay current on emerging technologies and techniques through self-directed study
• Support & Enablement (approx. 20% of time)
  • Respond to questions from partner teams and support functions
  • Monitor production systems and track Service Level Objectives
  • Review the performance, capacity, and prediction quality of live systems on an ongoing basis

Qualifications

• 1–3 years of hands‑on experience in software engineering or ML development
• Proficiency in Python or a comparable scripting language
• Familiarity with data engineering concepts and platforms such as BigQuery or similar cloud data stores
• Experience with web frameworks (e.g., Node.js) and front‑end technologies (HTML, CSS, JavaScript, React, D3)
• Comfort writing SQL against relational databases
• Working knowledge of Git or another version control system
• Exposure to ML concepts including clustering, forecasting, anomaly detection, and neural networks
• Understanding of foundational statistics and regression; familiarity with Bayesian methods is a plus
• Experience with standard ML tooling: Jupyter Notebooks, Pandas, SciPy, Scikit‑learn, Gensim, TensorFlow, or PyTorch
• Familiarity with a major cloud platform and its ML services (e.g., Vertex AI, BigQueryML, AutoML)
• Comfort working in Linux/Unix environments
• Exposure to CI/CD pipelines and REST API design
• Basic understanding of production system principles: availability, disaster recovery, performance, and security

Core Competencies

• Adapts well to shifting priorities and ambiguity
• Communicates clearly across technical and non-technical audiences
• Committed to continuous learning and self-improvement
• Collaborates effectively; brings a team-first mindset
• Drives outcomes with a results-oriented approach
• Builds positive, productive working relationships

We encourage all qualified applicants to apply, including people from traditionally underrepresented groups such as women, visible minorities, Indigenous peoples, people identifying as LGBTQ2SI, veterans, and people with visible/nonvisible disabilities.