Senior Machine Learning Engineer

Skyflow is hiring a Senior Machine Learning Engineer for a remote role open to applicants in India. Level rates it ; you can apply on Skyflow.

Level's read on this job

Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
This is listed as a remote role.
The posting mentions Vertex AI, PyTorch and TensorFlow among its tools and skills.
Level classes it as a senior role.
Skyflow has 10 other open roles on Level.

AI in this role

Build, deploy, and scale production machine learning models and end-to-end ML pipelines as a senior ML engineer.

vertex-aipytorchtensorflowscikit-learnmlflowsagemakerpythongonumpypandas

fine-tuningml-opscomputer-visionnlpmachine-learningmlopsdistributed-systemsdeep-learning

Skyflow secures the flow of data across datastores, models, and agents. Enterprises turn to Skyflow as their runtime AI data control layer to protect sensitive data, enable safe AI deployment, and unlock full value from their applications, data platforms, and AI systems.

Skyflow is trusted by Fortune 500 enterprises, and leading SaaS companies in financial services, healthcare, retail, travel and hospitality.

Skyflow is headquartered in Palo Alto, California and was founded in 2019. For more information, visit www.skyflow.com or follow on X and LinkedIn.

About the role:

We’re looking for a Senior Machine Learning Engineer to build, deploy, and scale ML models that directly impact product and business outcomes. You’ll work closely with product, backend, and data teams to turn real-world problems into production-ready ML solutions.This is a hands-on role where experimentation, ownership, and shipping matter more than academic perfection.

The ideal candidate has hands-on experience building end-to-end ML systems, deploying models to production, creating AI workflows, and improving the reliability, observability, and performance of AI-powered products.

We know great software engineers come from diverse backgrounds so no single individual may have all the desired skills on day one. But if you are the kind of software engineer who would have loved to engineer solutions for Stripe or Twilio APIs, or the Slack or Zendesk app, or the Snowflake or MongoDB platform - we want to talk to you.

You have:

8+ years of experience in Machine Learning.
Proficient in one of the programming languages either Go or Python
Design, develop, and deploy production ML models and AI services.
Build end-to-end ML pipelines for data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
Having experience in implementing AI-driven software systems with a focus on NLP, NER.
Hands-on experience with Python and ML libraries (NumPy, Pandas, Scikit-learn).
Experience with:
Experience with deep learning frameworks like TensorFlow or PyTorch
Deep understanding of SOTA ML model architectures, especially language models
Understanding of data pipelines, feature stores, and model lifecycle
Experience in performance engineering: developing high-throughput, low-latency systems
Experience with continuous integration, writing testable code, and test-driven development
Deep understanding of algorithms, data structures, scalability, and distributed systems
Privacy, authorization/authentication engineering is a huge plus.

Good to have:

Experience with NLP, recommendation systems, or computer vision
Exposure to MLOps tools (MLflow, Airflow, Kubeflow, SageMaker, Vertex AI)
Experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure)

You will:

Design, build, train, and deploy machine learning models for production use
Build end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, deployment
Experimenting with and rapid prototyping of open source ML models.
Fine tuning open source models for NLP and Vision tasks
Synthetic data generation, model validation
GPU Performance optimisation of large scale ML models, including transformers
Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality.
Monitor, detect, and mitigate risks unique to LLMs and agentic systems.
Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking.
Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path.
Responsible for designing and developing Privacy APIs and backend infrastructure to support large-scale data and privacy workflows
Optimize models for performance, scalability, and reliability
Contribute to performance engineering efforts and ensure low-latency and high-throughput transactions at scale.
Participate in building and implementing effective test strategies and developing software with high agility and zero downtime.

Benefits:

Work from home expense
Excellent Health Insurance Options
Very generous PTO
Flexible Hours

At Skyflow, we believe that diverse teams are the strongest teams. We invite applicants of all genders, races, ethnicities, nationalities, ages, religions, sexual orientations, disability statuses, educational experiences, family situations, and socio-economic backgrounds.

How we rate this

Senior Machine Learning Engineer at Skyflow rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Machine Learning Engineer is an open job at Skyflow.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in India and is marked remote.
The job was posted on September 1, 2026.
Seniority: senior.
Applications are handled by Skyflow. Apply on Skyflow

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningML OpsComputer visionNLPMachine learningMLOpsDistributed SystemsDeep learning

Also common in machine learning engineer jobs at this level

Generative AI C++Spark Reinforcement learning Java RAG

Pay

Typical machine learning engineer job: $199k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Fine-tuning, ML Ops, Computer vision, NLP, and Machine learning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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