Summary
What you’ll impact
The Senior MLOps Engineer will own and operate large‑scale cloud‑native pipelines that ingest and process multimodal sensor data for the organization’s crop‑spraying measurement platform. They will ensure model traceability, monitor drift, build diagnostic and deployment tooling, and collaborate with data scientists, engineers, and domain experts to maintain robust ML systems.
Responsibilities
What you'll do
- 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
Requirements
What you’ll bring
- 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
- 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