About this opportunity
Workana lists this Senior ML Engineer opportunity in indianapolis, Indiana. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Workana is the largest remote work platform for talent in Latin America. Our division focuses on matching exceptional professionals with leading and innovative companies around the globe.
About the Client
Our client is a U.S.-based organization operating in the life sciences and biotech domain, focusing on complex scientific data and machine learning systems. They are looking for a Senior Machine Learning Engineer with deep industry domain knowledge to join their technical initiatives.
While the client prefers a hybrid schedule of 3 days a week in Indianapolis, Indiana (EDT/EST time zone), they are open to fully remote candidates across the East Coast of the U.S. who can travel to Indianapolis occasionally as needed.
Role Overview
As a Senior Machine Learning Engineer , you will lead the architecture, integration, and scaling of machine learning capabilities within ongoing, production-grade software systems. This role sits at the intersection of machine learning, software engineering, and platform architecture, where your primary focus will be turning models into robust, scalable, and observable production systems rather than pure exploratory research.
You will work on an ongoing project, taking ownership of ML pipelines, model integration, and engineering quality. We are seeking a proactive professional with a great attitude who can drive technical execution, collaborate with domain experts, and deliver high-impact solutions.
Selection Process
The process consists of 3 stages:
Initial interview with Workana's recruiting team.
People interview with the client's team.
Technical Interview with the client's team (rapid fire style).
As Workana has multiple clients, if you pass the first round with Workana's recruiting team, you may also be considered for other relevant opportunities if the initial opportunity does not move forward.
Responsibilities
Own the architecture and implementation of production-grade ML systems and workflows.
Transition models from development and research into scalable production services.
Design and build reliable training, inference, evaluation, and deployment pipelines.
Integrate ML models into APIs, backend services, applications, and core product workflows.
Optimize ML systems for latency, throughput, scalability, reliability, and cost-efficiency.
Establish engineering standards for model versioning, testing, observability, and deployment.
Collaborate closely with domain experts, data scientists, and cross-functional teams with a strong, collaborative attitude.
Diagnose and resolve technical bottlenecks across the ML application stack.
Proven track record as a Senior Machine Learning Engineer with strong software engineering fundamentals.
Strong industry and domain knowledge within life sciences, biotech, or scientific datasets.
Advanced proficiency in Python and modern ML/software engineering practices.
Demonstrated experience deploying, scaling, and operating ML models in production environments.
Deep understanding of model inference, system design, microservices, and cloud-native workflows.
Strong collaborative mindset, excellent problem-solving ability, and a positive, proactive attitude.
Fluent English is mandatory, as the role involves daily interaction with U.S.-based stakeholders.
Must be based in the United States, with preference given to candidates who can work hybrid in Indianapolis, IN or travel to Indianapolis periodic.
Competitive salary with travel expenses covered when travel is required.
Flexible work arrangements (Hybrid in Indianapolis, IN, or Fully Remote within the U.S. East Coast with occasional travel).
Dynamic career growth with innovative, high-impact enterprise projects.
Long-term independent contractor agreement.
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Worksite address
indianapolis, IN, 46262, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.