Senior Machine Learning Operations Engineer

Own the architecture, execution, and operational excellence of large‑scale, cloud‑native pipelines for multimodal sensor data ingestion, processing, labeling, and validation.

Champion model traceability by building a clear lineage for every production model. Track what data trained it, what code produced it, what validation it passed, and how it's performing. Evaluate and recommend tooling for versioning, metadata, and model registry

Partner with data scientists to detect data quality issues, detect drift in upstream sources, and ensure features stay fresh and reliable

Track model drift over weeks, flag slow degradation before it crosses a threshold, surface feature freshness problems before they cascade

Build diagnostic tooling to root cause pipeline and recommendation issues quickly. Ensure the right context is logged at each stage, candidates, features, serving context, and building the dashboards to tie it collectively

Own automated gates that block bad deployments and assist in running model issue retrospectives

Work with ML engineers, data engineers, and stakeholders to coordinate on post-deployment metrics, defining what metrics to collect after deployment and why they matter

Build tooling and support non-technical domain experts in understanding perception system performance and identifying opportunities for pipeline improvement

Collaborate closely with cross-functional teams of software engineers, machine learning scientists, product specialists, and researchers to design, build, and maintain robust data pipelines grounded in sound data organization, domain knowledge, and careful analysis

Communicate technical findings, data characteristics, and limitations clearly and effectively to both internal partners and external collaborators

Required:

Bachelor’s or graduate degree in Computer Science, Electrical Engineering, or a closely related field

5+ years of experience building large-scale distributed systems, applications, or advanced ML systems‑scale distributed systems, applications, or advanced ML systems

Experience with MLOps, data pipelines, and cloud distributed systems

Proficiency in Python for system‑level and performance‑critical implementation

Experience operating end‑to‑end data or ML pipelines for reliability, scale, and observability

Communication skills that align collaborators and drive execution across functions

Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)

A record of ownership, accountability, and customer‑focused engineering

Proven track record of designing robust frameworks with high-quality, durable APIs

Deep understanding of machine learning algorithms with hands‑on application

Expertise in building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure‑performance

Robust SQL skills and comfort digging into data distributions, feature health, and model behavior

Preferred:

Experience with the field of agriculture or related fields such as environmental or life sciences

Experience with data science based on real-world physical sensors data

Experience with vision-based ML

Experience creating intuitive data visualization tools that make complex data approachable for non-technical users

Prior experience in developing machine-learning models relevant to biological or crop protection outcomes

Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands-on experience with PyTorch and/or TensorFlow, including training and deploying neural networks

Experience operating recommendation systems at scale