Senior Machine Learning Engineer

New

JobgetherArtificial intelligence

Remote work arrangement within the United States.Full-TimeSenior

SalaryBase salary range of $140,000–$215,000 per year for U.S. candidates. Eligibility for bonuses and equity grants.

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Job Details

Required Skills: AWSDockerPythonKubernetesMachine LearningData engineeringDistributed Systems

Have professional experience in data engineering and architecture supporting advanced data science or machine learning applications.
Have a deep understanding of LLM post-training techniques and the computational architectures required to support them.
Understand scalability and distributed systems concepts, including sharding, partitioning, concurrency, and large-scale inference.
Have experience with a high-level programming language such as Python or JVM-based technologies.
Have experience with cloud, containerization, and modern infrastructure technologies such as Docker, Kubernetes, AWS, GCP, or managed AI services.
Bring strong software engineering fundamentals, including testing strategies, code reviews, continuous integration, logging, monitoring, and resilient system design.
Be able to work in a test-driven, collaborative, and iterative development environment.
Demonstrate the ability to deliver maintainable software consistently and meet project commitments.
Demonstrate use of AI technologies to improve decision-making, streamline workflows, increase efficiency, or drive business outcomes.
Be willing to develop expertise in new technologies and cybersecurity concepts.
Experience scaling machine learning inference across GPUs or GPU clusters is highly relevant.
Familiarity with Kafka, Cassandra, Spark, Elasticsearch, Terraform, Chef, or Ansible is valuable.

Develop machine learning and data engineering solutions for applied data science initiatives.
Support LLM post-training, custom model development, evaluation workflows, and production implementation.
Design and maintain scalable data pipelines for machine learning and data science use cases.
Build customer-facing AI applications designed for significant scale and low latency.
Develop and maintain distributed systems for large-scale AI workloads and inference.
Own engineering work through development, testing, deployment, monitoring, and ongoing optimization.
Apply automated testing, peer code review, logging, observability, and resilient architecture practices.
Collaborate with data scientists, engineers, product teams, and other stakeholders on technical solutions.
Analyze systems and data to identify vulnerabilities, performance gaps, reliability issues, and improvement opportunities.

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