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Machine Learning Operations Engineer

External
modulate logoModulate · Somerville, MA
Full-timeHybrid2mo ago
AWSCloudFormationIncident ResponseLeadershipLinuxMachine Learning
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Responsibilities

  • Own the reliability and performance of ML model inference systems in production
  • Ensure high availability of deployed models across APIs and enterprise products
  • Build systems to handle scaling, load variability, and production traffic growth
  • Reduce operational burden through better tooling, automation, and processes
  • Help define how Modulate runs ML systems at scale with reliability and efficiency
  • Deploy, monitor, and maintain production machine learning inference systems
  • Oversee fleets of inference machines and ensure system health and performance
  • Design monitoring, alerting, and incident response systems for ML workloads
  • Participate in on-call rotations and lead incident response and debugging
  • Build systems and processes for scaling inference infrastructure under variable load
  • Improve reliability and observability of production ML services
  • Collaborate on infrastructure-as-code for production deployments
  • Support or contribute to GPU-based training and inference infrastructure
  • Work closely with ML and engineering teams to ensure smooth model deployments
  • (Optional growth area) Optimize model inference performance and latency

Requirements

  • Experience deploying and maintaining production software systems
  • Experience building monitoring and alerting systems for production environments
  • Experience with on-call rotations and incident response
  • Strong experience with AWS, Python, and Linux
  • Exposure to PyTorch or similar ML frameworks
  • Experience working with GPU-based applications and basic GPU tooling (drivers, runtime, monitoring)
  • Strong debugging and systems thinking skills
  • Ability to operate calmly in production incident environments
  • Experience with ML model serving systems or dedicated model servers
  • Experience monitoring GPU performance for inference workloads
  • Experience optimizing machine learning model inference
  • Familiarity with audio or multimedia data (codecs, streaming, real-time systems)
  • Experience with infrastructure-as-code (e.g., Terraform, CloudFormation)

Benefits

Competitive salary + equityFull health, dental, and vision coverageFlexible PTO with strong culture of taking itWeekly team lunches with dietary accommodationsHybrid work with core in-office days and flexible remote optionsLeadership and technical learning sessionsCareer development and continued learning supportUp to 8 weeks work-from-anywhere policyA deeply inclusive, human-centered culturePay TransparencyModulate believes in transparency as a cornerstone of equity and trust. Compensation for this role is based on seniority, skills, and experience.Salary: $150-$200KEquity: OfferedOther perks: HSA, FSA, 15 holidays, professional growth resourcesReady to join us? Apply here or reach out directly-we're excited to meet you.A quick note as you applyPlease apply through the website rather than emailing careers@modulate.aiFor application questions ("Your fit for the role," "Your values/goals," "Why Modulate?"), focus on relevant experience and motivationsAvoid including protected demographic informationKeep responses authentic and in your own voice

Additional Information

Modulate is the leader in conversational voice intelligence. We enable enterprises to deeply understand how people communicate and take timely action based on those insights. Our products help detect harm, prevent fraud, and build safer, more trusted online and real-world voice environments. We are building a Conversation Intelligence Platform - APIs, workflows, and applications that bring voice understanding to customers at enterprise scale. We're looking for a Machine Learning Operations Engineer to own and scale the production inference systems behind Modulate's machine learning models. This role will focus on ensuring high availability, reliability, and efficiency of deployed models across our APIs and enterprise products as we rapidly grow in customer usage and model demand.


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