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Linux Command Line Jobs in New York (NOW HIRING)

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Machine Learning Engineer, Evals

New York, NY · On-site

$153K/yr

Comfort with Git, CI/CD basics, Docker, and the Linux command line (SSH, tmux, debugging a remote job) * Understanding of basic eval statistics: why accuracy misleads on imbalanced judges, what Cohen ...

Peraton

Software Research Engineer

Basking Ridge, NJ

Linux/Command Line Terminal familiarity with scripting (Bash/Python) * HTML/CSS/JavaScript and related technologies * Network traffic generation tools, including Scapy * Network emulation frameworks ...

Betterment

Sr. Product Security Engineer

New York, NY · Hybrid

$175K - $205K/yr

Experience with the Linux command line interface * Experience with securing AWS cloud infrastructure (EC2, RDS, S3, VPCs) * Experience with any of these technologies is a plus: GraphQL, React ...

Peraton

Software Research Engineer

Basking Ridge, NJ · On-site

Linux/Command Line Terminal familiarity with scripting (Bash/Python) * HTML/CSS/JavaScript and related technologies * Network traffic generation tools, including Scapy * Network emulation frameworks ...

Octaura

Platform Support Engineer

New York, NY · On-site

$100K - $150K/yr

Linux command line * Shell scripting * Java * Python * SQL * Nagios and/or Splunk * Ticketing systems such as Jira or ServiceNow The base pay range for this position in New York is $100,000 - $150 ...

Photon

Sr Angular Developer - Jersey City

Somerville, NJ · On-site

$56 - $68.50/hr

Experience in Linux/Unix/Mac OS X based environments working with command line tools (CLI) & scripting * Experience with Bootstrap or similar frameworks * Experience using CSS Preprocessors ...

Photon

Angular Developer - Jersey City

Somerville, NJ

$56 - $68.50/hr

Experience in Linux/Unix/Mac OS X based environments working with command line tools (CLI) & scripting * Experience with Bootstrap or similar frameworks * Experience using CSS Preprocessors ...

MABSTOA

Computer Specialist III, Software

New York, NY · On-site

Proficiency in using the Linux command line for file management, process monitoring, and system configuration * Basic skills in managing user permissions, software installation, and troubleshooting ...

MABSTOA

Computer Specialist III, Software

Queens, NY

$122K - $138K/yr

Proficiency in using the Linux command line for file management, process monitoring, and system configuration * Basic skills in managing user permissions, software installation, and troubleshooting ...

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Is it hard to get a Linux Command Line job?

Getting a Linux Command Line job typically requires strong knowledge of Linux operating systems, command-line tools, scripting, and sometimes certifications like CompTIA Linux+. Entry-level positions are accessible with relevant skills and experience, but advanced roles may demand specialized expertise and experience working with Linux environments. Building a solid foundation in Linux fundamentals and gaining hands-on experience can improve job prospects.

What jobs are there in Linux Command Line?

Jobs related to Linux Command Line include system administrator, DevOps engineer, network administrator, and security analyst. These roles require proficiency with command-line tools, scripting, and system management in Linux environments, often involving certifications like CompTIA Linux+ or RHCE.

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Infographic showing various Linux Command Line job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 79% Full Time, 15% Part Time, 4% Contract, and 1% Nights. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution.

Machine Learning Engineer, Evals

NOUS RESEARCH

New York, NY • On-site

$153K/yr

Full-time

Re-posted 2 days ago

Job description

You'll work across the lab on agent capability evals, benchmark design, LLM-as-judge systems, failure analysis, and the infrastructure that ties it together. This is a high-growth, high-ownership role on a small team, and you'll ship evaluation infrastructure that researchers depend on from day one.
Responsibilities:
  • Run the full eval pipeline end to end and reproduce known results during onboarding, pairing with a senior engineer on your first task
  • Build a judge calibration protocol: sample human-labeled decisions, measure agreement (κ, per-class P/R), identify drift zones, and document it so anyone can re-run it
  • Extend an existing benchmark (GAIA, τ-Bench, SWE-bench slice, etc.) with new tasks targeting known capability gaps, including the prompt, environment, rubric, automated grader, and QA
  • Run failure analysis on model outputs: categorize failure modes, quantify prevalence, and write up findings with recommendations for training data, judge prompts, or benchmark changes
  • Own a recurring eval workflow (weekly regression suite, judge drift dashboard, red-team evaluation for a new capability) and ship tooling researchers actually use

Qualifications:
  • 3+ years in software engineering, ML engineering, data science, or a research-adjacent role, with concrete evaluation experience from coursework, an internship, a side project, open source work, or a job
  • Experience with at least one LLM evaluation framework (Harbor, Nemo Evaluator, etc.), with real opinions on what it does well and where it falls short
  • Hands-on experience with LLMs: prompting, few-shot design, and ideally fine-tuning or RAG; regular use of coding agents
  • Solid Python. You write clean, tested, version-controlled code that a colleague could run without you babysitting it
  • Comfort with Git, CI/CD basics, Docker, and the Linux command line (SSH, tmux, debugging a remote job)
  • Understanding of basic eval statistics: why accuracy misleads on imbalanced judges, what Cohen's κ measures, how to think about confidence intervals on a metric
  • At least 3 of the following: you can explain why LLM-as-judge needs calibration; you've done failure analysis and can tell model bugs apart from prompt, grader, or retrieval issues; you know at least two agent benchmarks (GAIA, AgentBench, τ-Bench, MINT, SWE-bench, WebShop, ALFWorld) and a limitation of each; you've designed or extended an eval dataset with happy paths, edge cases, and adversarial examples; you've thought about non-determinism in eval, how you sample, how many runs, how you report variance
  • You communicate clearly to both researchers and engineers, in the right language for each
  • You're comfortable with ambiguity, can turn a half-formed request into a plan, and know when to ask for help

Preferred:
  • RLVR / RLHF pipeline experience
  • Training data curation experience
  • Distributed eval orchestration experience
  • Benchmark design from scratch
  • Red teaming and adversarial eval experience
  • Familiarity with psychometrics or measurement theory

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