Fractal
MLOps Engineer
New York · Full-time
Sponsorship not specified$120k-$140kDetected 4 days ago
PythonFastAPICode ReviewGitSQLRedisDatabricksDockerKubernetesCI/CDGitHub ActionsJenkinsKafkaRabbitMQMachine LearningPyTorchscikit-learnSparkAirflowData EngineeringData ScienceLLMsMLOpsCommunication
About the role
- It's fun to work in a company where people truly BELIEVE in what they are doing!
- We're committed to bringing passion and customer focus to the business.
- Where no possibility is written off, only challenged to get better.
Requirements
- Hands-on Databricks experience including working knowledge of MLFlow and fluency with distributed compute in Spark.
- Working experience with common ML libraries (scikit-learn, XGBoost, PyTorch or similar) - enough to be a competent partner to data scientists, not necessarily to build novel models.
- Comfort reading and refactoring batch ML or data pipeline code - understanding intent and edge cases before rewriting.
- All applications must be made through posted job openings
- Required
- Deep hands-on Python in using it for both data engineering and application development, and comfort across SQL, PySpark, and shell scripting.
- Production experience building services with FastAPI (or a comparable Python web framework), including auth, validation, error handling, and observability.
- Experience building queue-based asynchronous processing systems - familiarity with at least one of Kafka, RabbitMQ, SQS, Redis Streams, Celery, or equivalent - and the operational concerns that come with them (retries, idempotency, back-pressure, dead-letter queues).
- Strong Docker and general containerization skills
- comfortable with Kubernetes concepts even if a platform team runs the cluster.
- Strong grasp of the end-to-end ML lifecycle and a track record of building or migrating feature engineering code with an explicit focus on training / batch / real-time parity.
- CI/CD (Jenkins, GitHub Actions, or equivalent), version control workflows, and orchestration (Airflow, Prefect, or equivalent).
- Excellent written and verbal communication
- able to drive alignment with data scientists, platform engineers, and business stakeholders without a manager brokering every conversation.
Nice to have
- Experience operationalizing LLM-based systems - inference serving, evaluation, cost and latency controls.
- The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets
- experience and training
- licensure and certifications
- and other business and organizational needs.
- At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.
- A reasonable estimate of the current range is: $120,000 to $140,000 Yearly.
Compensation
- The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.
- At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.
- A reasonable estimate of the current range is: $120,000 to $140,000 Yearly.
Benefits
- You will be eligible for benefits on the first day of employment with the Company.
- The Company provides 11 paid holidays and 12 weeks of Parental Leave.
- We also follow a "free time" PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.
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This listing is sourced directly from Fractal's careers page and normalized into a canonical job model.