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Remote Audio Machine Learning Jobs in Connecticut

The Assistant Vice President (AVP), Applied AI leads data science, traditional machine learning ... This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office ...

Research General Engineer

New London, CT · On-site +1

$120K - $156K/yr

Specific competencies include remote sensing, machine learning, computer vision, control systems, uncrewed systems, advanced networking, test engineering, and integration of autonomy into legacy or ...

You will then deploy what your team builds in remote events for the top networks and brands in ... audio path, fiber linked systems, digital video compression and transmission standards, PC ...

Jr. Systems Engineer - Hybrid

Stamford, CT · On-site +1

$55K - $75K/yr

... learning and accelerated technical growth. This role will provide direct support to client users ... Provide onsite and remote IT support to client users. * Troubleshoot and resolve desktop, software ...

Showing results 21-35

Remote Audio Machine Learning information

What is a remote audio machine learning?

A Remote Audio Machine Learning job involves using machine learning techniques to analyze, process, or generate audio data while working from a remote location. Professionals in this field develop algorithms for tasks such as speech recognition, music classification, noise reduction, or audio synthesis. They often work with large datasets, build and train models, and collaborate with teams online. These roles typically require skills in programming, signal processing, and experience with machine learning frameworks.

What are the key skills and qualifications needed to thrive as a remote audio machine learning engineer?

To thrive as a Remote Audio Machine Learning Engineer, you need strong foundations in digital signal processing, machine learning algorithms, and programming (often Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, and audio processing libraries (e.g., LibROSA), as well as experience with cloud platforms, is highly valuable. Excellent problem-solving skills, self-motivation, and clear remote communication are essential soft skills for collaborating across distributed teams. These competencies enable the development of robust, innovative audio ML solutions while ensuring effective teamwork and project delivery in a remote setting.

How does a remote audio machine learning role typically collaborate with cross-functional teams, and what communication tools are commonly used?

In a Remote Audio Machine Learning position, collaboration with cross-functional teams such as software engineers, data scientists, and product managers is essential. Regular communication is maintained through tools like Slack, Zoom, and project management platforms such as Jira or Trello. Team members often participate in virtual stand-ups, sprint planning sessions, and code reviews to ensure alignment on project goals and timelines. Effective asynchronous communication and clear documentation are especially important in remote settings to keep everyone informed and foster a productive workflow.

What is the difference between Remote Audio Machine Learning vs Remote Audio Engineer?

AspectRemote Audio Machine LearningRemote Audio Engineer
Required CredentialsBackground in machine learning, data science, or AI; often a degree in computer science or related fieldsAudio engineering, sound design, or music production degree or certification
Work EnvironmentPrimarily focused on developing algorithms, data analysis, and model training, often in a tech or research settingRecording, mixing, editing audio, often in studios or remote production setups
Employer & Industry UsageTech companies, research labs, AI startups working on audio recognition or enhancementMusic, film, broadcasting, and media production companies

Remote Audio Machine Learning specialists focus on developing algorithms to process and analyze audio data, while Remote Audio Engineers handle the practical aspects of recording and editing sound. Both roles may collaborate but serve different functions within the audio industry.

What are the most commonly searched types of Audio Machine Learning jobs in Connecticut?

The most popular types of Audio Machine Learning jobs in Connecticut are:

What cities in Connecticut are hiring for Remote Audio Machine Learning jobs?

Cities in Connecticut with the most Remote Audio Machine Learning job openings:

AVP Applied AI

The Hartford

Hartford, CT • On-site, Remote

Full-time

Re-posted 4 days ago


The Hartford rating

8.8

Company rating: 8.8 out of 10

Based on 121 frontline employees who took The Breakroom Quiz

56th of 311 rated insurance


Job description

AVP Data Science - GD05AE

We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

The Assistant Vice President (AVP), Applied AI leads data science, traditional machine learning, and agentic AI capabilities supporting The Hartford's Business Insurance. This role partners closely with underwriting, product, actuarial, and technology leaders to deliver scalable, production ready models and AI driven decision systems that support complex risks, bespoke products, and profitable growth across specialty markets.This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Must be eligible to work in the US without company sponsorship.Primary Job Responsibilities
  • Own delivery, performance, and risk outcomes for one or more large, complex Applied AI portfolios spanning multiple teams, domains, or lines of business. Translate enterprise and businessunit AI priorities into multiyear portfolio roadmaps and investment plans.
  • Ensure applied AI solutions deliver measurable business value while meeting standards for security, reliability, explainability, fairness, safety, and cost efficiency across solution types including generative and agentic AI, retrievalaugmented systems, forecasting, recommendation systems, anomaly or fraud detection, and multimodal use cases.
  • Lead and develop Sr. Directors and Directors. Build leadership bench strength through succession planning, coaching, and capability development. Ensure consistent application of the Applied AI operating model, decision rights, delivery discipline, and escalation paths across the portfolio. Reinforce shared expectations for quality, evaluation rigor, and production readiness.
  • Provide portfoliolevel technical direction and rigorous oversight, partnering closely with Principal ICs, Architecture, AI Platform, and Centers of Excellence. Ensure consistent adoption of approved AI standards, patterns, and guardrails.
  • Review and thoughtfully evaluate portfoliolevel architectural choices, evaluation approaches, production readiness, and operational risk signals, guiding leaders through disciplined tradeoffs across quality, grounding, latency, cost, scalability, and regulatory risk.
  • Accountable for consistent application of evaluation and monitoring practices across the portfolio. Ensure evaluation frameworks span classification, information retrieval, RAG/chat, forecasting, and customer or operational KPIs. Oversee governance of metric taxonomies, thresholds, validation evidence, gold and synthetic test sets, A/B testing practices, drift detection, failuremode analysis, and incident response expectations. Ensure evaluation results inform prioritization, release decisions, and risk management at the executive level.
  • Set portfoliolevel expectations and governance for unstructured data and retrieval practices, including document ingestion pipelines, parsing, OCR, layoutaware extraction, metadata and lineage management, access controls, PII detection and redaction, and auditability. Ensure retrieval strategy decisions, including embedding approaches, hybrid and dense retrieval patterns, reranking, grounding validation, and multilingual considerations, align with enterprise standards and regulatory requirements.
  • Accountable for portfolio-level AI governance ensuring alignment with Legal, Compliance, Model Risk, Privacy, Security, and Audit partners. Maintain readiness for audits and regulatory review by ensuring governance artifacts, controls, escalation paths, and operational evidence are consistently established and enforced. Escalate material risks, tradeoffs, and investment decisions to VPs with clear options and implications.
  • Partner with senior leaders across Product, Technology, Operations, Claims, Underwriting, Finance, and HR to align Applied AI delivery with business outcomes. Influence portfolio funding, prioritization, and workforce planning through evidencebased assessments of delivery performance, evaluation outcomes, and risk considerations.
  • Oversee portfoliolevel planning, dependencies, resourcing, and financial stewardship. Adjust plans to address shifting priorities, capacity constraints, emerging technical risks, or regulatory changes. Drive continuous improvement in delivery effectiveness, operational resilience, governance maturity, and value realization across the Applied AI portfolio.
Skills
  • Demonstrated experience leading large, complex Applied AI portfolios in regulated enterprise environments.
  • Proven ability to lead Sr. Directors and Directors, building durable leadership capacity and consistent operating discipline across organizations.
  • Strong technical and regulatory fluency across applied AI, including generative and agentic AI, retrievalaugmented systems, evaluation and monitoring practices, and production AI operations, sufficient to review, inform, and govern seniorlevel decisions.
  • Applied understanding of unstructured data and retrieval approaches, including document ingestion pipelines, OCR, layoutaware extraction, embeddings, hybrid and dense retrieval, reranking, metadata and lineage management, and PII controls.
  • Deep familiarity with AI governance, model risk management, responsible AI practices, and compliancebydesign expectations.
  • Demonstrated success translating strategy into coordinated execution and investment decisions across multiple teams over multiyear horizons.
  • Ability to influence VPs and senior partners through clear, datadriven communication of technical tradeoffs, evaluation outcomes, portfolio risks, and business impact.
Education, Experience, Certifications and Licenses
  • 12+ years of applicable experience with a Bachelor's degree; fewer years may be accepted with a higher degree. Master's or Ph.D. preferred in Machine Learning, Applied Mathematics, Data Science, Computer Science, or a similar analytical field, or progress towards a relevant professional designation.
  • 7-10+ years leading leaders, large portfolios, or complex programs.

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

$182,400 - $273,600

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

About Us|Our Culture|What It's Like to Work Here|Perks & Benefits


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About Hartford

Sourced by ZipRecruiter

Hartford Financial Services Group, widely recognized as The Hartford, is a renowned company based in Hartford, CT, US. Established in 1810, it has evolved into an industry leader in the insurance and financial services sector, proudly serving more than one million businesses in the US. The Hartford is committed to offering a gamut of insurance products that include homeowners, automobile, and business insurance as well as employee benefits and mutual funds. The company’s core values revolve around customer-focused innovations, diversity and inclusion, and ethical dealings that have earned them a customer-centric reputation. This shapes their mission which revolves around aiding their clients to overcome unforeseen obstacles and enhancing their wealth over time. Among the company's noted accomplishments is being consistently listed among the World's Most Ethical Companies, a testament to their unwavering commitment towards responsible business practices.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

Hartford, CT, US

Year founded

1810

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