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About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely.
3 Month Contract | Remote | High-Impact
About the Role: We’re looking for an infrastructure-focused devOps / MLOps engineer who thrives at the intersection of machine learning, systems, and product delivery. This is a hands-on 3-month contract role responsible for deploying, monitoring, and scaling the testing and deployment infrastructure for a real-time ML-powered content moderation system used to detect and triage abuse, threats, and edge-case language.
In This Role, You Will:
- Design, build, and document a maintainable GCP cloud infrastructure CI/CD pipeline for real-time model serving and data workflows
- Deploy and optimize APIs for low-latency ML systems
- Automate model deployment, retraining, and evaluation (CI/CD for ML)
- Build observability tooling to monitor rollouts, errors, integration testing, and drift in ML pipelines
- Ensure infrastructure meets security, compliance, and uptime requirements
We’re Looking for Someone Who:
- Has 3–8 years of DevOps/Platform engineering experience deploying machine learning systems or high-availability backend systems.
- Ability to build CI/CD pipelines from scratch; familiarity with GitHub Actions or similar.
- Expert-level proficiency with Git and GitHub workflows and strong scripting abilities in Python, Bash, and/or Go.
- Experience with Google Cloud Run and Docker. Experience with Google Cloud Platforms, Docker, Kubernetes, Terraform .
- Familiarity with SOC 2 compliance requirements and security best practices (IAM, secrets, etc).
- Experience implementing monitoring, logging, and alerting systems (e.g., Prometheius, Grafana, ELK/EFK, OpenTelemetry).
- Can work cross-functionally with ML, security, and engineering teams to deploy safely and iterate fast.
- Brings a builder's mindset and bias for ownership in ambiguous environments.
What Success Looks Like in the 3 Months:
- You’ve deployed and monitored a real-time ML inference system with well-defined observability.
- You’ve implemented an API with latency under 1000ms for classifier-based inference.
- You’ve partnered with ML engineers to streamline deployment and retraining workflows.
- You’ve built logging and monitoring that gives insight into system performance and classifier behavior.
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
