Staff Software Engineer, ML Platform
$169,000–$273,000 year
Remote · United States or US
Job Summary
Staff ML Platform Engineer role to elevate Afresh's core ML platform to higher performance, reliability, and scale. You will contribute to infrastructure powering ML and applied science teams, enabling rapid innovation and production-grade deployment for models across forecasting, demand estimation, and ordering policies. Key responsibilities include shaping multi-quarter technical strategy for ML platform gaps, delivering a platform-level initiative for real-time inference, redesigning model configuration and deployment end-to-end, and mentoring senior engineers. The role emphasizes building and maintaining scalable Python-based tooling, data APIs, feature pipelines, distributed inference layers, and experiments-capable infrastructure to support forecasting and optimization workloads at scale.
Required Qualifications
- BS in Computer Science or a relevant technical field
- 7+ years of professional software development experience
- Experience collaborating with ML engineers, data scientists, or applied scientists on large-scale ML projects
- Strong interest in ML modeling (e.g., demand forecasting, state estimation, ordering policy)
- Understanding of bridging research notebooks and production-grade software
- Technical leadership experience and ability to mentor junior engineers
- Deep expertise in library design, API design, data structures, and algorithms
- Strong familiarity with Python ecosystem (NumPy, Pandas, Torch, PySpark)
- Experience architecting high-throughput distributed inference systems using Spark, Dask, or Ray
- Experience with end-to-end orchestration (lineage tracking, resource scheduling, self-healing pipelines)
- Experience engineering robust data architectures with focus on schema evolution and performance tuning
- Experience with distributed systems and scalable infrastructure
- Experience with Databricks or similar data warehousing environments
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