ML Infrastructure Engineer, Later
$145-165k
Influencer marketing and social media management
Be an early applicant
Influencer marketing and social media management
1001+ employees
Be an early applicant
$145-165k
1001+ employees
Company mission
To help the world's top brands create high-performing social content and collaborate with creators to reach new audiences, drive engagement, and generate predictable ROI.
Role
Who you are
- If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity
- 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems
- Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads
- Proven track record designing and implementing CI/CD pipelines for ML systems
- Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools
- Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases
- Experience managing container orchestration platforms (Kubernetes, EKS, or GKE)
- Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus)
- Experience working with infrastructure-as-code tools such as Terraform or CloudFormation
- Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms
- Experience managing GPU-based workloads and scaling training/inference systems
- Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines
- Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks
- A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments
What the job involves
- We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio
- As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale
- This role is critical to accelerating our data science initiatives and enabling future AI innovation
- You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence
- Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives
- Establish best practices for model lifecycle management, deployment standards, monitoring, and governance
- Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development
- Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy
- Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask)
- Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies
- Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana
- Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets
- Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments
- Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation
- You'll partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows
- Translate data science experimentation needs into production-ready infrastructure solutions
- Serve as the technical bridge between ML experimentation and productized deployment
- Share knowledge and best practices to elevate ML maturity across teams
- You'll stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency
- Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate
- Contribute to governance, reproducibility, and auditability standards for ML systems
- Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity
- What success looks like:
- ML models move from experimentation to production quickly and reliably, with minimal manual intervention
- CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies
- Model performance, drift, and infrastructure health are proactively monitored and observable
- Infrastructure supports scalable GPU training and real-time inference without bottlenecks
- Data scientists report improved velocity, reproducibility, and confidence in deploying models
- ML systems are secure, compliant, and aligned with evolving product and AI strategy
Our take
Platforms like Instagram, Pinterest, and TikTok have become indispensable for businesses of all sizes, shaping their marketing strategies and driving success. Later specializes in assisting customers in navigating these channels. Offering a visually oriented suite of tools, the marketing platform enables marketing managers to schedule, analyze, and track sales from their content effectively.
Focusing on small businesses, Later is doubling down on its strategy, aiming to provide additional products tailored for marketers at these enterprises while expanding its geographic reach. This expansion of product capabilities not only caters to the evolving needs of small businesses but also helps Later mitigate platform risk from social media companies mimicking its services. Partnerships with platforms like TikTok and Instagram, featuring post scheduling, analytics, and conversation tools, showcase Later's commitment to innovation and staying ahead in the competitive landscape.
Now, the company is entering an exciting new chapter with updates designed to help marketers and creators stay ahead of what's next in social. The platform now offers enhanced analytics, improved collaboration tools, expanded support for platforms like Threads and Snapchat, and smarter insights through innovations such as Future Trends. These developments show Later's commitment to making social media management more intuitive, data-driven, and impactful so brands and creators can build stronger connections and lead the next era in social storytelling.
Steph
Company Specialist at Welcome to the Jungle
Insights
6% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Jan 2015
$1.2m
SEED
Dec 2014
$1.3m
SEED
Total funding: $3.8m
Company benefits
- Learning & development opportunities
- Market competitive compensation
- Hybrid work environment
- Flexible paid time off
- Competitive healthcare benefits
Company HQ
Government Center, Boston, MA
Leadership
Scott Sutton
(CEO)Scott joined lAter in 2023 as COO, becoming CEO in 2024. Previously, he spent 4 years at ZoomInfo, joining as VP of Revenue Operations before becoming Senior VP of Operations and then CBO.
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