> Markdown version of [/jobs/ext/3702230-software-engineer](https://www.wearedevelopers.com/jobs/ext/3702230-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - **Company:** MYLES DRIVEN LLC - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Airflow, Amazon Web Services, Data Architecture, Python (Programming Language), Machine Learning, Standard Sql, Azure Machine Learning, Service Design, Software Deployment, Software Engineering, Software Systems, Strategies of Testing, Feature Store, Real Time Systems, Feature Engineering, Apache Spark, Model Validation, Caching, Parallel Computation, Rate Limiting, Pyspark, Real-time Inference, Apache Kafka, Machine Learning Operations, Video Streaming, Unsupervised Learning, Databricks - **Published:** October 12, 2026 - **Apply:** https://www.juju.com/job/16_1ba770932 ## About the Role * 5+ years of software engineering experience * Experience building ML platform, MLOps, model training, model deployment, or feature pipeline systems * Strong Python experience * Strong software design, testing, and platform engineering fundamentals * Proficiency with SQL * Hands-on experience with Spark or PySpark * Strong understanding of ML fundamentals, including probability, statistics, supervised and unsupervised learning, feature engineering, validation strategies, model evaluation, drift, stability, and monitoring * Experience with modern data and ML infrastructure such as AWS, Databricks, MLflow, model registries, model serving, Airflow, or similar orchestration tools * Experience building real-time systems, including service design, caching, rate limiting, backpressure, and low-latency architecture * Experience building batch pipelines at scale * Practical knowledge of feature store concepts, including offline and online stores, backfills, point-in-time correctness, experiment tracking, and evaluation frameworks * Strong ownership mindset and proactive approach to platform reliability * Excellent communication and collaboration skills across engineering and data science teams Bonus Experience * Deep Databricks experience, including MLflow, workflows, lakehouse architecture, or model serving * Experience with feature stores such as Tecton, Feast, or similar platforms * Experience with streaming technologies such as Kafka or Kinesis * Experience in fintech, risk, lending, underwriting, or regulated financial systems * Familiarity with model safety checks, rejection flows, override flows, and auditability * Experience with A/B testing platforms, shadow deployments, canary releases, and automated rollback * Experience building low-latency inference systems ## Description Our client is hiring a Senior Software Engineer, ML Platform to lead the evolution of its machine learning platform. This person will design, build, and maintain the core systems that support model development, production deployment, batch inference, real-time inference, feature stores, observability, and underwriting infrastructure. You'll work closely with Data Science and Platform Engineering to turn research workflows into reliable software systems. This is a strong fit for an engineer who enjoys building developer-friendly platforms, creating clean abstractions, and owning infrastructure that powers real business decisions. What You'll Do * Own and evolve the company's ML platform end-to-end * Turn data science notebooks into reusable, tested, production-ready software components * Build libraries, pipelines, templates, SDKs, and CLIs that help data scientists move faster * Create developer-friendly abstractions for feature definition, model training, evaluation, deployment, and monitoring * Build and scale low-latency real-time model serving infrastructure * Expand batch ML inference systems across scheduling, parallelism, cost controls, observability, failure handling, and rollback * Own and improve the feature store, including offline and online feature definitions * Design systems for high read/write throughput and consistent offline/online semantics * Instrument training and inference workflows for latency, throughput, accuracy, drift, data quality, and cost * Build alerting, dashboards, and observability systems for platform health * Support production underwriting systems across batch and real-time workflows * Partner with Data Science on model interfaces, SLAs, safety checks, and product integrations * Drive incident response, postmortems, and long-term reliability improvements