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Job Overview
Location
Indiana, USA
Job Type
Full-time
Category
Software Engineering
Date Posted
October 24, 2025
Full Job Description
đź“‹ Description
- • Own the complete machine-learning lifecycle at Celaralabs—from raw data ingestion to production-grade inference at scale—ensuring every model you ship delivers measurable business impact and delights end-users.
- • Architect, train, and deploy state-of-the-art models (classical, deep-learning, and generative) that power core product features, internal automation, and new revenue streams, while continuously monitoring performance and drift in real time.
- • Design resilient, high-throughput data pipelines using Python, Spark, and SQL that transform messy, high-volume datasets into clean, feature-rich inputs ready for both offline experimentation and online serving.
- • Champion MLOps excellence by implementing automated CI/CD workflows, containerized training jobs, model registries, and rollback strategies that cut release cycles from weeks to hours and eliminate manual bottlenecks.
- • Build and validate rapid Proof-of-Concepts that translate ambiguous business ideas into quantifiable ML opportunities, presenting findings to stakeholders with clear success metrics and go/no-go recommendations.
- • Optimize models for latency, memory footprint, and throughput across CPU, GPU, and edge deployments, shaving milliseconds off inference time and reducing cloud costs without sacrificing accuracy.
- • Collaborate daily with data engineers, DevOps, product managers, and domain experts in a fully remote, async-first culture, ensuring seamless integration of models into microservices, APIs, and customer-facing applications.
- • Stay on the cutting edge—evaluate new research papers, open-source libraries, and cloud services—then distill insights into internal tech talks, reusable code templates, and production-ready enhancements that keep Celaralabs ahead of the curve.
- • Establish robust monitoring, alerting, and retraining workflows that detect data drift, concept drift, and performance degradation before they impact customers, ensuring 99.9 % uptime and continuous model improvement.
- • Document every experiment, pipeline, and deployment decision in clear, searchable artifacts so future teammates can reproduce results, extend functionality, and onboard rapidly.
🎯 Requirements
- • 3+ years shipping ML systems in production with demonstrable impact on user experience or business KPIs.
- • Expert-level Python (Pandas, NumPy, Scikit-Learn, XGBoost, PyTorch/TensorFlow) and solid SQL/Spark for large-scale data manipulation.
- • Hands-on experience with at least one major cloud (AWS, GCP, or Azure) and containerized deployment (Docker/Kubernetes).
- • Proven use of MLOps tooling such as MLflow, SageMaker, or Vertex AI for CI/CD, experiment tracking, and model governance.
- • Nice-to-have: experience with feature stores, time-series modeling, or generative AI techniques.
🏖️ Benefits
- • Fully remote, async-first culture—work from anywhere with flexible hours and no commute.
- • Competitive contract rates with the possibility of long-term extensions and equity upside.
- • Annual learning & conference stipend plus dedicated time for open-source contributions and research.
- • Home-office setup budget and premium health & wellness allowance.
Skills & Technologies
About Celara Labs Inc.
Celara Labs builds AI-driven software that accelerates scientific research and drug discovery. Its platform integrates machine learning with lab automation to design, execute and analyze high-throughput experiments, enabling biotech and pharmaceutical teams to iterate faster on therapeutic candidates. The company focuses on generative models, robotic cloud labs and data infrastructure that reduce cycle times from hypothesis to validated results. Founded by ex-Google, Stanford and Genentech engineers, the team partners with R&D organizations to turn complex biology into reproducible, data-rich workflows.
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