Janea Systems
Senior Software Engineer- Machine Learning
Remote Senior Machine Learning Engineer role with clear candidate location fit.
PostedJul 2, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Senior Software Engineer- Machine Learning
Requirements and responsibilities
Readable role content extracted into sections for faster review.
Details
- To be considered for this position, you must have the following qualifications:Bachelor's or Master’s degree in Computer Science or a related field4+ years of experience as a Software Engineer, Platform Engineer, ML Engineer, Data Scientist, AI Engineer, or Data EngineerFlexibility in experience with different programming languages and willingness to adjust to project needsStrong knowledge of PythonKnowledge of machine learning algorithms, data pre-processing methods, and ML frameworks (such as PyTorch, TensorFlow, Keras)Experience with containers and Kubernetes in cloud environments (AWS, MS Azure, or GCP)Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo)Understanding of software testing, benchmarking, and continuous integration principlesAbility to translate business needs into technical requirementsExcellent communication and problem-solving skills, with the ability to break down complex challenges and develop innovative solutionsBeing self-motivated and adaptable, with the ability to work effectively in fast-paced, dynamic environmentIdeal candidates will also have:Familiarity with agent frameworks (such as Langchain, Langgraph, IllamaIndex)Familiarity with developing RAG systemsExperience with Natural Language Processing (NLP)Familiarity with monitoring tools (such as DataDog or Langfuse)Any associate cloud certification (AWS preferred)Responsibilities:Design scalable data pipelines and infrastructure for enterprise ML systemsImplement ML models and systems into productionCollaborate with data scientists and software engineersDeploy scalable tools and services for machine learning training and inferenceEvaluate new technologies to improve ML system performance and reliabilityApply software engineering best practices, including CI/CD, to ML developmentFacilitate the development and deployment of ML proof-of-conceptsReview, refactor, optimize, containerize, deploy, version, and monitor ML modelsImplement monitoring and alerting solutions to ensure the reliability and performance of machine learning systemsOptimize and automate the machine learning deployment process to ensure efficiency and reproducibilityCollaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deploymentsStay updated with industry trends and apply knowledge to drive innovationPromote industry best practices and enhance team expertiseWhy join Janea? Because world-class talent deserves world-class opportunities. What we offer:Competitive compensation with benefits, paid vacation, and sick leave.The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.
- Bachelor's or Master’s degree in Computer Science or a related field
- 4+ years of experience as a Software Engineer, Platform Engineer, ML Engineer, Data Scientist, AI Engineer, or Data Engineer
- Flexibility in experience with different programming languages and willingness to adjust to project needs
- Strong knowledge of Python
- Knowledge of machine learning algorithms, data pre-processing methods, and ML frameworks (such as PyTorch, TensorFlow, Keras)
- Experience with containers and Kubernetes in cloud environments (AWS, MS Azure, or GCP)
- Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo)
- Understanding of software testing, benchmarking, and continuous integration principles
- Ability to translate business needs into technical requirements
- Excellent communication and problem-solving skills, with the ability to break down complex challenges and develop innovative solutions
- Being self-motivated and adaptable, with the ability to work effectively in fast-paced, dynamic environment
- Familiarity with agent frameworks (such as Langchain, Langgraph, IllamaIndex)
- Familiarity with developing RAG systems
- Experience with Natural Language Processing (NLP)
- Familiarity with monitoring tools (such as DataDog or Langfuse)
- Any associate cloud certification (AWS preferred)
- Design scalable data pipelines and infrastructure for enterprise ML systems
- Implement ML models and systems into production
- Collaborate with data scientists and software engineers
- Deploy scalable tools and services for machine learning training and inference
- Evaluate new technologies to improve ML system performance and reliability
- Apply software engineering best practices, including CI/CD, to ML development
- Facilitate the development and deployment of ML proof-of-concepts
- Review, refactor, optimize, containerize, deploy, version, and monitor ML models
- Implement monitoring and alerting solutions to ensure the reliability and performance of machine learning systems
- Optimize and automate the machine learning deployment process to ensure efficiency and reproducibility
- Collaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deployments
- Stay updated with industry trends and apply knowledge to drive innovation
- Promote industry best practices and enhance team expertise
- Competitive compensation with benefits, paid vacation, and sick leave.
- The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.
- Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.
- An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.
- Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.
- Bachelor's or Master’s degree in Computer Science or a related field
- 4+ years of experience as a Software Engineer, Platform Engineer, ML Engineer, Data Scientist, AI Engineer, or Data Engineer
- Flexibility in experience with different programming languages and willingness to adjust to project needs
- Strong knowledge of Python
- Knowledge of machine learning algorithms, data pre-processing methods, and ML frameworks (such as PyTorch, TensorFlow, Keras)
- Experience with containers and Kubernetes in cloud environments (AWS, MS Azure, or GCP)
- Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo)
- Understanding of software testing, benchmarking, and continuous integration principles
- Ability to translate business needs into technical requirements
- Excellent communication and problem-solving skills, with the ability to break down complex challenges and develop innovative solutions
- Being self-motivated and adaptable, with the ability to work effectively in fast-paced, dynamic environment
Ideal candidates will also have:
- Familiarity with agent frameworks (such as Langchain, Langgraph, IllamaIndex)
- Familiarity with developing RAG systems
- Experience with Natural Language Processing (NLP)
- Familiarity with monitoring tools (such as DataDog or Langfuse)
- Any associate cloud certification (AWS preferred)
Responsibilities:
- Design scalable data pipelines and infrastructure for enterprise ML systems
- Implement ML models and systems into production
- Collaborate with data scientists and software engineers
- Deploy scalable tools and services for machine learning training and inference
- Evaluate new technologies to improve ML system performance and reliability
- Apply software engineering best practices, including CI/CD, to ML development
- Facilitate the development and deployment of ML proof-of-concepts
- Review, refactor, optimize, containerize, deploy, version, and monitor ML models
- Implement monitoring and alerting solutions to ensure the reliability and performance of machine learning systems
- Optimize and automate the machine learning deployment process to ensure efficiency and reproducibility
- Collaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deployments
- Stay updated with industry trends and apply knowledge to drive innovation
- Promote industry best practices and enhance team expertise
Responsibilities:
- Competitive compensation with benefits, paid vacation, and sick leave.
- The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.
- Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.
- An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.
- Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.
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