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# Senior Software Engineer - ML Infrastructure

- **Company:** Latitude Inc
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Salary:** $179,200.0 - $268,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Systems Engineering, Cloud Computing, Code Review, Encodings, Computer Engineering, Data Files, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, System Testing, Management of Software Versions, Pytorch, Evaluation Pipelines, Delivery Pipeline, Deep Learning, Pandas, Scikit Learn, Information Technology, Xgboost, Build Tools, Machine Learning Operations, Streamlit Framework, Data Pipelines
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-ml-infrastructure-palo-alto-ca--7c6da69b-ef35-42e4-b091-0730f8e65de4

## About the Role

Software Engineering, Artificial Intelligence (AI), Machine Learning, Robotics, Computer Systems, Cloud Computing, Safety Engineering, System Test, Systems Engineering, System Operations, * Bachelor''s degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field and 4+ years of relevant industry experience (or Master''s degree and 2+ years of relevant experience).

 * Strong Python software engineering skills, including designing modular, maintainable, well-tested systems in a shared codebase.
 * Experience building end-to-end ML systems, from data pipelines and dataset curation through training, evaluation, and production, using tools such as SQL, pandas, PyTorch, or scikit-learn.
 * Experience designing scalable ML infrastructure, such as training and evaluation pipelines, experiment tracking, model serving, or distributed jobs.
 * Solid grounding in statistics and experimental design, including choosing evaluation strategies, metrics, and thresholds that support real-world decisions.
 * Clear communication and the ability to work effectively across software, ML, and autonomy teams.

Nice to Have:

 * Experience with probabilistic forecasting, sequential, or graph-based models, as well as classical methods such as gradient boosting.
 * Experience with calibration, uncertainty estimation, ablation studies, error analysis, or release-over-release comparison.
 * Experience with data-centric ML, such as active learning, auto-labeling, embedding-based search, or dataset versioning.
 * Experience with orchestration, experimentation, and internal tooling such as Dagster, Airflow, Ray, Weights & Biases, Dash, or Streamlit.
 * Experience with Bazel or other large-scale build systems.
 * Autonomous driving or robotics experience is helpful but not required.

## Description

This role is a great fit for a software engineer who enjoys building reliable, well-tested Python systems and takes pride in producing ML results that people can trust. Day to day, the work is a mix of data and ML infrastructure, model development and evaluation, and internal tools used by partner teams.

What You'll Do:

 * Curate datasets. Build pipelines that discover, label, and organize driving scenarios from real-world and simulated data into balanced, high-quality training and evaluation sets.
 * Build data pipelines. Design and maintain scalable, reproducible data pipelines with versioning, provenance, well-designed train/validation/holdout splits, and clear data contracts.
 * Turn data into insight. Explore dataset composition, coverage, and label quality; characterize how behavior evolves across releases and scenario types; and share findings that help shape model, dataset, and autonomy priorities.
 * Scale ML infrastructure. Build training, inference, and evaluation pipelines that run smoothly at scale, with robust data loading, checkpointing, and experiment tracking.
 * Develop and ship models. Train, tune, and productionize learned models, from classical approaches to deep learning, and integrate them into automated analytics pipelines.
 * Own the metrics. Define, implement, and validate model and release metrics such as precision, recall, F1, ROC AUC, calibration quality, and task-specific metrics, with strong statistical rigor.
 * Run thoughtful experiments. Set up baselines, ablations, cross-validation, and holdout comparisons; quantify uncertainty; and communicate results that help the team make confident decisions.
 * Build tools engineers love. Develop dashboards and services that make it easy to compare software versions, explore results at the scenario level, visualize model outputs, and gather feedback from reviewers.
 * Raise the engineering bar. Design clean interfaces between data, modeling, and product layers; champion testing, type checking, code review, and fast, dependable CI.
 * Partner across autonomy. Collaborate with autonomy, simulation, data, and infrastructure teams to take ideas from prototype to production.