Agency hiring for a client through a fast-lane process. Permanent full-time position. US work authorization is needed due to the Public Trust Clearance requirement.
General Requirements
- Experience - 7 to 8 years of overall professional experience in data science, analytics, or a related field
- At least 2–3 years of hands-on experience specifically focused on Large Language Models (LLMs) and related techniques (e.g., fine-tuning, instruction tuning, prompt engineering).
- Ability to obtain a Public Trust Clearance (Required for the role)
- Must be authorized to work in the United States.
Technical Proficiency:
- Coding knowledge and experience with at least one of the following languages: Python, C, R, Java, Scala, with strong proficiency in Python being highly preferred.
- Proven ability to use statistical computer languages (Python, R, SQL, etc.) for data manipulation, analysis, and model development.
- Knowledge of ML and data mining techniques (Regression, Deep Learning, NLP, Time Series Analysis, Social Media Analysis, etc.).
- Familiarity with AWS services (Athena, S3, Glue, SageMaker, Comprehend, Bedrock) for scalable model development.
- Preferred: exposure to MLOps tools, big data technologies (Hadoop, Spark), or other cloud services.
LLM-Focused Skills (Desired, not all required):
- PEFT (LoRA/QLoRA) for efficient model fine-tuning.
- Instruction Fine-tuning, Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT)/Tree-of-Thought (ToT).
- Quantization, Pruning, and Knowledge Distillation strategies for model optimization.
- Experience with Hugging Face Transformers, LangChain, Llama Index, and/or large-scale training frameworks. - Familiarity with LLM evaluation, interpretability, and best practices.
AUTOMATTIC | SYSTEMS ENGINEER | REMOTE | FULL TIME | Proficiency with some or all of: NGINX, Docker, PHP, Golang, LUA, MySQL, ELK.
We’d love to chat with you if you have:
* Experience with implementing and supporting Kubernetes-based infrastructure.
* Experience working on a large-scale system to make it performant, highly scalable, and fault-tolerant.
* Experience in Linux systems administration and a deep understanding of how web-related services (load balancers, high availability solutions, web servers, databases, caching daemons, etc.) work.
* Proficiency in information systems concepts, system administration, and cloud platforms.
* Proficiency with some or all of: NGINX, Docker, PHP, Golang, LUA, MySQL, ELK.
* Working knowledge of network technologies such as: TCP/IP stack, IPv6, DNS, Anycast, firewalls (iptables), and TLS.
* A passion for open source software.
* A willingness to learn!
AUTOMATTIC | SYSTEMS ENGINEER | REMOTE | FULL TIME | Proficiency with some or all of: NGINX, Docker, PHP, Golang, LUA, MySQL, ELK.
We’d love to chat with you if you have:
* Experience with implementing and supporting Kubernetes-based infrastructure.
* Experience working on a large-scale system to make it performant, highly scalable, and fault-tolerant.
* Experience in Linux systems administration and a deep understanding of how web-related services (load balancers, high availability solutions, web servers, databases, caching daemons, etc.) work.
* Proficiency in information systems concepts, system administration, and cloud platforms.
* Proficiency with some or all of: NGINX, Docker, PHP, Golang, LUA, MySQL, ELK.
* Working knowledge of network technologies such as: TCP/IP stack, IPv6, DNS, Anycast, firewalls (iptables), and TLS.
Did you try toptal.com? Toptal offers heaps of projects, and every single client is vetted by developers that work for Toptal before the job is posted. Basically, they guarantee continuous quality work for remote developers. Changed my life!
Thanks for the link. Matches my experience quite closely:
1) They refused to set the rate I asked for. It was "too much for someone from Eastern Europe", so they set it to half that.
2) Despite going along with that, I never managed to get any contract through them - probably because they don't focus on C# that much. Even when I find something interesting and apply, I usually don't hear back for at least a couple of weeks.
It is extremely possible that I'm just not a good fit for the jobs, or that I am indeed asking for too much, but so far my experience with the site has been... underwhelming.
Hei there, I am just reading up and I think it's extremely unfortunate. Luckily, I have witnessed different experiences. I especially appreciate that they are running an internal Slack where community members are supporting each other and several Toptal employees are usually available for feedback on job applications one has currently running. Regarding rates, I cannot complain, eastern European here as well :)
Agency hiring for a client through a fast-lane process. Permanent full-time position. US work authorization is needed due to the Public Trust Clearance requirement.
General Requirements - Experience - 7 to 8 years of overall professional experience in data science, analytics, or a related field
- At least 2–3 years of hands-on experience specifically focused on Large Language Models (LLMs) and related techniques (e.g., fine-tuning, instruction tuning, prompt engineering).
- Ability to obtain a Public Trust Clearance (Required for the role)
- Must be authorized to work in the United States.
Technical Proficiency:
- Coding knowledge and experience with at least one of the following languages: Python, C, R, Java, Scala, with strong proficiency in Python being highly preferred.
- Proven ability to use statistical computer languages (Python, R, SQL, etc.) for data manipulation, analysis, and model development.
- Knowledge of ML and data mining techniques (Regression, Deep Learning, NLP, Time Series Analysis, Social Media Analysis, etc.).
- Familiarity with AWS services (Athena, S3, Glue, SageMaker, Comprehend, Bedrock) for scalable model development.
- Preferred: exposure to MLOps tools, big data technologies (Hadoop, Spark), or other cloud services.
LLM-Focused Skills (Desired, not all required): - PEFT (LoRA/QLoRA) for efficient model fine-tuning. - Instruction Fine-tuning, Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT)/Tree-of-Thought (ToT). - Quantization, Pruning, and Knowledge Distillation strategies for model optimization. - Experience with Hugging Face Transformers, LangChain, Llama Index, and/or large-scale training frameworks. - Familiarity with LLM evaluation, interpretability, and best practices.