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. Raise is currently hiring a contract team member on behalf of our client. They’re expanding their team to meet growing needs, making this a unique opportunity to work with an industry leader. 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 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 strive to build teams that reflect the diversity of the communities we work in. 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. We have a dedicated webpage for accommodations where you can learn more about what we offer, and request accommodation: #J-18808-Ljbffr HOME DEPOT STORE SUPPORT, INC.