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Compression Engineer Jobs in New York (NOW HIRING)

Beacon Software Inc

Forward Deployed Engineer

New York, NY · On-site

$141K/yr

The 11th deployment in a home-services SaaS should be 10x faster than the 1st, and that compression is your job as much as the engineers building the platform. * Carry the GM relationship. You are ...

NAVA Software Solutions

ETL Developer

Jersey City, NJ · On-site

$53 - $69.50/hr

NAVA Software solutions is looking for a ETL Developer Details: ETL Developer Location: Jersey city ... compression, Direct path loads etc. and that maximizes re-usable components and services that ...

Kepler

Agentic Systems Engineer

New York, NY · On-site

$250K - $350K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • PTO

Our founding engineers led Foundry's core systems - Ontology, Fusion, Workshop, FoundryML, created ... Context compression, prompt optimization, model routing, and latency reduction. Make agents faster ...

Paramount

Sr Live Video Quality Software Engineer

Manhattan, NY · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Do you have a strong background in video streaming and compression? Does sharing your knowledge and collaborating across multiple teams in a DevOps culture sound exciting to you? If you answered yes ...

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Compression Engineer information

What is the difference between Compression Engineer vs Mechanical Engineer?

AspectCompression EngineerMechanical Engineer
Required CredentialsBachelor's in Mechanical, Aerospace, or related fields; certifications like PE or ASME often preferredBachelor's or higher in Mechanical Engineering; PE license beneficial
Work EnvironmentDesign and testing of compression systems, working in labs or manufacturing settingsDesign, analysis, and manufacturing of mechanical systems across various industries
Industry UsageOil & gas, HVAC, power generation, manufacturingAutomotive, aerospace, manufacturing, energy
Common Search/ComparisonYesYes

While both roles require a strong background in mechanical principles, a Compression Engineer specializes in designing and testing compression systems like turbines or compressors, often within energy or manufacturing sectors. Mechanical Engineers have a broader scope, working on various mechanical systems across multiple industries. The roles overlap in skills and credentials but differ in focus and application.

What does a compression engineer do?

A compression engineer designs, analyzes, and improves compression systems used in various industries such as oil and gas, manufacturing, and power generation. They work with equipment like compressors, turbines, and valves, often using simulation tools and adhering to safety standards. Their role involves troubleshooting, optimizing performance, and ensuring reliable operation of compression machinery.

What are popular job titles related to Compression Engineer jobs in New York?

For Compression Engineer jobs in New York, the most frequently searched job titles are:

What job categories do people searching Compression Engineer jobs in New York look for?

The top searched job categories for Compression Engineer jobs in New York are:

What cities in New York are hiring for Compression Engineer jobs?

Cities in New York with the most Compression Engineer job openings:

Compression Engineer jobs near you
Infographic showing various Compression Engineer job openings in New York as of August 2026, with employment types broken down into 87% Full Time, and 13% Contract. Highlights an 85% In-person, and 15% Remote job distribution.

Speech Recognition Engineer

Ova Technologies

Manhattan, NY • On-site

Other

Re-posted 11 days ago

Job description

Job Title: Speech Recognition Engineer
Job Summary
We are seeking a Speech Recognition Engineer to design, develop, and optimize Automatic Speech Recognition (ASR) systems for voice-enabled applications. The ideal candidate will have expertise in speech processing, deep learning, natural language processing (NLP), and machine learning. This role involves building, training, fine-tuning, and deploying speech recognition models that deliver high accuracy, low latency, and robust performance across diverse languages, accents, and acoustic environments.
Key Responsibilities
  • Design, develop, and optimize Automatic Speech Recognition (ASR) models for production applications.
  • Build end-to-end speech processing pipelines, including audio preprocessing, feature extraction, decoding, and post-processing.
  • Train, fine-tune, and evaluate speech recognition models using large-scale speech datasets.
  • Improve recognition accuracy for multilingual, domain-specific, and noisy audio environments.
  • Develop real-time and batch speech recognition solutions.
  • Optimize models for latency, throughput, memory efficiency, and inference performance.
  • Integrate ASR models into voice assistants, conversational AI systems, call center platforms, and enterprise applications.
  • Develop data pipelines for speech data collection, annotation, augmentation, and quality validation.
  • Evaluate model performance using industry-standard speech recognition metrics.
  • Collaborate with NLP Engineers, Machine Learning Engineers, AI Engineers, Data Scientists, and Product teams.
  • Deploy speech recognition models using MLOps and cloud-native deployment practices.
  • Monitor production performance and continuously improve model quality.
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Speech Technology, or a related field.
  • 3+ years of experience in speech recognition, speech processing, machine learning, or AI engineering.
  • Strong programming skills in Python.
  • Experience with deep learning frameworks such as PyTorch or TensorFlow.
  • Solid understanding of digital signal processing (DSP) fundamentals.
  • Experience with speech processing libraries such as SpeechBrain, ESPnet, Hugging Face Transformers, torchaudio, librosa, or Kaldi.
  • Experience training and fine-tuning deep learning models.
  • Familiarity with Linux development environments, Git, and containerization using Docker.
  • Understanding of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
Preferred Qualifications
  • Experience with modern ASR architectures such as Whisper, Conformer, wav2vec 2.0, DeepSpeech, or RNN-Transducer (RNN-T).
  • Experience deploying speech recognition models using ONNX Runtime, TensorRT, NVIDIA Triton Inference Server, or TorchServe.
  • Knowledge of multilingual and low-resource language speech recognition.
  • Experience with streaming speech recognition and real-time inference.
  • Familiarity with speech enhancement, voice activity detection (VAD), speaker diarization, and keyword spotting.
  • Experience with MLOps tools such as MLflow, Kubeflow, or cloud AI platforms.
  • Knowledge of Large Language Models (LLMs) for speech understanding and conversational AI.
Technical Skills
  • Python
  • PyTorch
  • TensorFlow
  • Hugging Face Transformers
  • SpeechBrain
  • ESPnet
  • Kaldi
  • torchaudio
  • librosa
  • Whisper
  • wav2vec 2.0
  • Conformer
  • RNN-T
  • ONNX Runtime
  • TensorRT
  • NVIDIA Triton Inference Server
  • TorchServe
  • Docker
  • Git
  • Linux
  • AWS / Azure / Google Cloud Platform
Soft Skills
  • Strong analytical and problem-solving skills
  • Excellent communication and collaboration
  • Attention to detail
  • Ability to work with cross-functional teams
  • Continuous learning mindset
  • Strong documentation and experimentation practices
Nice to Have
  • Experience with speech synthesis (Text-to-Speech) or conversational AI platforms
  • Knowledge of multilingual ASR evaluation and benchmarking
  • Experience with edge AI deployment for speech applications
  • Familiarity with model compression, quantization, and inference optimization
  • Publications or contributions in speech AI, ASR, or related open-source projects
Key Performance Indicators (KPIs)
  • Word Error Rate (WER) and Character Error Rate (CER)
  • Model inference latency and throughput
  • Speech recognition accuracy across languages and accents
  • Production model availability and reliability
  • Improvement in recognition quality over baseline models
  • Successful deployment and adoption of ASR features
  • Reduction in production defects and model regressions

Location
Hybrid / Remote / On-site (as applicable)
Employment Type
Full-time

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