Senior ML Engineer, Pathway
ML Infrastructure / ML DevOps
Real-time data analysis
Be an early applicant
Real-time data analysis
21-100 employees
Be an early applicant
21-100 employees
Company mission
To help software engineers to focus on developing code logic, without having to worry about the way data changes over time.
Role
Who you are
- Former or current Linux / systems / network administrator who is comfortable living in the shell and debugging at OS and network layers (systemd, filesystems, iptables/security groups, DNS, TLS, routing)
- 5+ years of experience in DevOps/SRE/Platform/Infrastructure roles running production systems, ideally with high‑performance or ML workloads.
- Deep familiarity with Linux as a daily driver, including shell scripting and configuration of clusters and services
- Strong experience with workload management, containerization, and orchestration (Slurm, Docker, Kubernetes) in production environments
- Solid understanding of CI/CD tools and workflows (GitHub Actions, GitLab CI, Jenkins, etc.), including building pipelines from scratch
- Hands-on cloud infrastructure experience (AWS, GCP, Azure), especially around GPU instances, VPC/networking, storage, and managed ML services (e.g., SageMaker HyperPod, Vertex AI)
- Proficiency with infrastructure as code (Terraform, CloudFormation, or similar) and a bias toward automation over manual operations
- Experience with monitoring and logging stacks (Grafana, Prometheus, Loki, CloudWatch, or equivalents)
- Familiarity with ML pipeline and experiment orchestration tools (MLflow, Kubeflow, Airflow, Metaflow, etc.) and with model/version management
- Solid programming skills in Python, plus the ability to read and debug code that uses common ML libraries (PyTorch, TensorFlow) even if you are not a full‑time model developer
- Strong ownership mindset, comfort with ambiguity, and enthusiasm for scaling and hardening critical infrastructure for an ML‑heavy environment
- Willingness to learn
What the job involves
- We are looking for a Senior ML Infrastructure / DevOps Engineer who loves Linux, distributed systems, and scaling GPU clusters more than fiddling with notebooks
- You will own the infrastructure that powers our ML training and inference workloads across multiple cloud providers, from bare‑bones Linux to container orchestration and CI/CD
- You will sit close to the R&D team, but your home is production infrastructure: clusters, networks, storage, observability, and automation
- Your work will directly determine how fast we can train, ship, and iterate on models
- Operate and scale GPU‑heavy clusters used daily by the R&D team for large‑scale training and low‑latency inference.
- Design, build, and automate the ML platform rather than just run pre‑defined playbooks.
- Work across multiple major cloud providers, solving interesting problems in networking, scheduling, and cost/performance optimization at scale
- Design, operate, and scale GPU and CPU clusters for ML training and inference (Slurm, Kubernetes, autoscaling, queueing, quota management)
- Automate infrastructure provisioning and configuration using infrastructure‑as‑code (Terraform, CloudFormation, cluster‑tooling) and configuration management
- Build and maintain robust ML pipelines (data ingestion, training, evaluation, deployment) with strong guarantees around reproducibility, traceability, and rollback
- Implement and evolve ML‑centric CI/CD: testing, packaging, deployment of models and services
- Own monitoring, logging, and alerting across training and serving: GPU/CPU utilization, latency, throughput, failures, and data/model drift (Grafana, Prometheus, Loki, CloudWatch)
- Work with terabyte‑scale datasets and the associated storage, networking, and performance challenges
- Partner closely with ML engineers and researchers to productionize their work, translating experimental setups into robust, scalable systems
- Participate in on‑call rotation for critical ML infrastructure and lead incident response and post‑mortems when things break
Our take
Machine learning has immense implications for reporting when used on a company's data, but despite its speed, accuracy and thoroughness, it can't currently be applied to incoming data. Put simply, the infrastructure to make this possible is missing, and without this, it's impossible to analyse data in real-time.
Pathway seeks to fix this, providing a streaming data processing layer that transforms complex, raw event data streams in real-time, allowing enterprises to derive insights in real-time. Reducing latency by over 900x compared to traditional means of data cleansing and transformation, it is currently used in logistics operations by companies such as DB Schenker and La Poste, but has applications in financial fraud detection, marketing and IoT usage tracking, among many others.
Founded by women, the company is something of a rarity in the male-dominated European deep tech space, and the Pathway's founders are something of a role model for female engineers. This has only strengthened the founders' resolve to succeed, and with an innovative product meeting a pressing business need, Pathway is poised to achieve this.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (2 rounds)
Nov 2024
$10m
SEED
Dec 2022
$4.5m
SEED
Total funding: $14.5m
Company benefits
- Intellectually stimulating work environment. Be a pioneer: you get to work with a new type of data processing framework
- Work in one of the hottest data/AI startups in France, with exciting career prospects
- Responsibilities and ability to make significant contribution to the company’ success
- Inclusive workplace culture
Company HQ
Downtown North, Palo Alto, CA
Leadership
Zuzanna Stamirowska
(CEO)Previously Co-Chair of the Logistics and Supply Chain Working Group for AIOTI, a spin-off of the European Commission. Before that, they were a PhD Researcher at Université Panthéon-Sorbonne and a Senior Sales Manager for Sphere Research Labs.
Claire Nouet
(Co-Founder & COO)Previously an Analyst at Veolia, a Parliamentary Assistant for the French National Assembly, and a Legal Assistant with Thales.
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